publications

133 journal articles, 67 conference articles, 3 books and 3 book chapters.

2025

Journal Articles

1. PCB Layout-Based Spatio-Temporal Graph Convolution Network for Anomaly Prediction in Solder Paste Printing Binkun Liu, Yun-Bo Zhao , Yu Kang, Yang Cao, and Zhenyi Xu IEEE Transactions on Components, Packaging and Manufacturing Technology 2025 [Abs] [doi] [pdf]

   Predicting solder paste printing anomaly on the printed circuit board (PCB) can improve first-pass yield and reduce rework costs. Considering the impact of PCB layout on the quality of solder paste printing, we propose a PCB layoutbased spatio-temporal graph convolution network, in which we construct a graph to model the spatial distribution of solder pads. Specifically, since the printing quality is related to the spatial distribution of the pads, we convert the PCB to a graph according to the Pearson correlation of the printing quality and then trim the edges of the graph with a correlation threshold to model the spatial distribution of solder pads. To model the timevarying physicochemical properties of the solder paste, normalize the production time, calculate the attention of the production time, and reconstruct the printing quality based on the attention. And then, we devise a weighted loss to improve the performance of predicted printing defective products due to the scarcity of defective products. Ultimately, the predicted printing quality is compared with the inspection threshold to estimate degree of anomaly. The proposed method is validated on six-day of real solder paste printing data, improving the average F1 score by 0.057 and the average accuracy by 0.022 for three typical anomalous printing behaviours over two temporal prediction scales.

2. Real-Time Detector on SMT Chip Component Defect Detection Zhenyi Xu, and Yun-Bo Zhao Int. J. of System Control and Information Processing 2025 [Abs] [pdf]

   In the Surface Mounted Technology (SMT) process, defect detection technology is the key to controlling the quality of electronic products. Industry usually uses AOI technology based on image processing, but it has many problems such as slow response speed and high defect misdetection rate. In recent years, there have been several researchers applying deep learning-based object detection algorithms to SMT defect detection scenarios. However, the current one-stage algorithms lack in accuracy, and the speed of the two-stage algorithms can not meet the demand for real-time detection in industrial scenarios. To solve these problems, we propose a new lightweight real-time algorithm SMT-DETR for defect detection of SMT chip components. Firstly, we design an adaptive collaborative feature extraction module (ACBlock), which has high sensitivity to the deformation features of the object, and can pay attention to the defective changes of the chip components effectively. Secondly, a new IMIoU loss is proposed to capture the tiny object information more effectively and has faster convergence speed by combining the state-of-the-art IoU loss function. Finally, we construct an SMT chip component defect dataset (SMTC). Experiments show that the proposed method is outperform the SOATs on SMTC and is also competitive on some public datasets.

3. Dual Flexible Prescribed Performance Control of Input Saturated High-Order Nonlinear Systems Yangang Yao, and Yun-Bo Zhao IEEE Transactions on Cybernetics 2025 [pdf]
4. Sliding Flexible Prescribed Performance Control for Input Saturated Nonlinear Systems Yangang Yao, Yu Kang, Yunbo Zhao, Jieqing Tan, Lichuan Gu, and Guolong Shi IEEE Trans. Fuzzy Syst. 2025 [Abs] [doi] [pdf]

   The issue of sliding flexible prescribed performance control (SFPPC) of input saturated nonlinear systems (ISNSs) is first studied in this article. Compared to the traditional PPC (TPPC) and the finite-time PPC (FTPPC) algorithms for ISNSs, under which the performance constraint boundaries (PCBs) present the symmetrical or asymmetric “horn” shape, which leads to a large jitter in the tracking error before the system reaches steady state; and once the parameters are selected, the PCBs are fixed, when the initial state (or reference signal) changes, it is necessary to re-verify whether the initial error still satisfies the initial constraint conditions. By designing a new pair of sliding flexible PCBs (SFPCBs) associated with the initial error, a novel SFPPC algorithm is presented in this article, which presents two main advantages: 1) the SFPCBs can slide adaptively with the initial tracking error without increasing the measure of the initial PCBs, implying that the proposed SFPPC algorithm can be applied to ISNSs with arbitrary initial errors without sacrificing the initial control performance; 2) the proposed SFPPC algorithm achieves a trade-off between performance constraint and input saturation, i.e., the SFPCBs can adaptively increase when the control input exceeds the maximum allowable threshold, effectively avoiding singularity, and when the control input is within the saturation threshold range, the SFPCBs can adaptively revert back to the original PCBs. The results demonstrate that the proposed SFPPC approach can guarantee that the system output tracks the desired signal within a prescribed time, and the tracking error always kept within the SFPCBs that depend on initial error, input and output constraints. The developed algorithm is exemplified by means of simulation instances.

5. Cross-Sensor Generative Self-Supervised Learning Network for Fault Detection Under Few Sample Huijuan Zhu, Yun-Bo Zhao , Xiaohui Yan, Yu Kang, and Binkun Liu Journal of Systems Science & Complexity 2025 [Abs] [pdf]

   In this paper, a cross-sensor generative self-supervised learning network is proposed for fault detection of multi-sensor. By modeling the sensor signals in multiple dimensions to achieve correlation information mining between channels to deal with the pretext task, the shared features between multisensor data can be captured, and the gap between channel data features will be reduced. Meanwhile, in order to model fault features in the downstream task, the salience module is developed to optimize cross-sensor data features based on a small amount of labeled data to make warning feature information prominent for improving the separator accuracy. Finally, experimental results on the public datasets FEMTO-ST dataset and the private datasets SMT shock absorber dataset(SMT-SA dataset) show that the proposed method performs favorably against other STATE-of-the-art methods.

6. Event-Based Enhancing Prescribed Performance Control for Stochastic Non-Triangular Structure Nonlinear Systems: A MTBFs-Based Approach Yuandong Zhu, Yangang Yao, Yu Kang, Yunbo Zhao, Jieqing Tan, Lichuan Gu, and Xuexiu Liang Nonlinear Dyn 2025 [Abs] [doi] [pdf]

   An event-based enhancing prescribed performance control (EPPC) algorithm for stochastic non-triangular structure nonlinear systems (NTSNSs) is first proposed in this article. The application of the mean value theorem enables the transformation of the stochastic NTSNS into an equivalent model characterized by affine structure. By introducing a pair of monotonic tube boundary functions (MTBFs) related to the initial error, an EPPC algorithm is presented, and a significant advantage over standard PPC (SPPC) and finite-time PPC (FTPPC) methods is that the proposed EPPC algorithm can simultaneously take into account multiple key indicators, including the steady state accuracy, convergence time and overshoot, which are widely demanded in actual control system. In addition, an adaptive event triggering mechanism is constructed to reduce the communication burden. Compared with the existing event triggering control (ETC) algorithm, the designed adaptive ETC algorithm can adjust the trigger threshold adaptively according to the tracking performance, so as to achieve the balance between tracking performance and trigger frequency. The results show that the proposed algorithm can ensure that all closed-loop signals are semi-globally bounded in probability, the system output can track the desired signal within a prescribed time, and the tracking error is consistently maintained within the monotonic tube-type constraint boundaries. The developed algorithm is exemplified through simulation instances.

Conference Articles

1. Vision Mamba-Based Approach for Incomplete Boundary Document Image Rectification Weihao Zhang, Xin Xia, Maopeng Li, and Yunbo Zhao In ICASSP 2025 2025 [Abs] [pdf]

   Capturing document images using handheld mobile devices often results in geometric deformations, which adversely affect the accuracy of Optical Character Recognition (OCR) and document understanding. However, existing transformer-based methods face significant computational costs when processing document images on resource-constrained devices. This study proposes an enhanced Vision Mamba architecture to learn the structural information of document images, thereby rectifying deformed images while reducing computational resource consumption. Additionally, owing to the relative positioning between the document and the imaging device, captured images may exhibit incomplete boundaries. Conventional learning-based methods are primarily designed for images with complete boundaries, which can diminish correction effectiveness. To address this issue, mask consistency loss and preprocessing techniques are introduced to improve the rectification of document images with incomplete boundaries. Experimental results demonstrate the effectiveness and superiority of this method, highlighting its significant value for intelligent document processing.

2024

Journal Articles

1. Modelling and Optimizing Motherboard Functional Testing in Laptop Manufacturing Peng Bai, Yu Kang, Kangcheng Wang, Yunbo Zhao, and Shaojie Dong J Syst Sci Complex 2024 [Abs] [doi] [pdf]

   Functional testing is key to fulfill quality control in laptop manufacturing and is of great economic value. However, due to the unavailability of practical data, mathematical model and systematic perspective, it has barely been touched from the academic community to date. For the first time, this work provides technical understanding of the key principles of functional testing, mathematically models the general framework, elucidates existing testing strategy under the proposed framework and model, and finally proposes a specified optimization strategy which outperforms existing strategies. This work lays the model foundation for the further optimization of functional testing, and can be regarded as a good example of how a systematic approach can solve practical industrial challenges.

2. Deep Reinforcement Learning for Maintenance Optimization of Multi-Component Production Systems Considering Quality and Production Plan Ming Chen, Yu Kang, Kun Li, Pengfei Li, and Yun-Bo Zhao Quality Engineering 2024 [Abs] [doi] [pdf]

   In this article, the maintenance optimization of multi-component production systems is investigated by considering quality and production plan. On the one hand, the downtime determined by the production plan provides opportunities for reducing maintenance costs; on the other hand, the deterioration of product quality induced by poor health state leads to extra loss. The coupled relations between production plan, quality, and maintenance, as well as the dependence between multiple components, pose challenges for maintenance optimization. To overcome these challenges, a novel decision model and a deep reinforcement learning-based solving method are proposed. Specifically, in addition to the degradation states of all components, the remaining time of the current batch related to the production plan is also treated as the system state, and the quality loss related to the degradation states is added to the reward function. The deep Q-network algorithm is employed, solving the maintenance optimization problem that considers quality and production plan. The effectiveness of the proposed method is validated by a numerical experiment.

3. Autonomous Multi-Drone Racing Method Based on Deep Reinforcement Learning Yu Kang, Jian Di, Ming Li, Yunbo Zhao, and Yuhui Wang Sci. China Inf. Sci. 2024 [Abs] [doi] [pdf]

   Racing drones have attracted increasing attention due to their remarkable high speed and excellent maneuverability. However, autonomous multi-drone racing is quite difficult since it requires quick and agile flight in intricate surroundings and rich drone interaction. To address these issues, we propose a novel autonomous multi-drone racing method based on deep reinforcement learning. A new set of reward functions is proposed to make racing drones learn the racing skills of human experts. Unlike previous methods that required global information about tracks and track boundary constraints, the proposed method requires only limited localized track information within the range of its own onboard sensors. Further, the dynamic response characteristics of racing drones are incorporated into the training environment, so that the proposed method is more in line with the requirements of real drone racing scenarios. In addition, our method has a low computational cost and can meet the requirements of real-time racing. Finally, the effectiveness and superiority of the proposed method are verified by extensive comparison with the state-of-the-art methods in a series of simulations and real-world experiments.

4. A Reflectance-Correction Retinex Framework for Thermal Image Enhancement in Nondestructive Defect Detection of CFRP Wei Liu, Pengwei Zhao, Yunbo Zhao, Yuqiang Fu, Jiahao Dai, and Le Zhou Measurement 2024 [Abs] [doi] [pdf]

   To ensure the quality of materials, pulsed thermography (PT) is an effective technique to detect the presence of internal defects in carbon fiber reinforced polymers (CFRP). However, non-uniform thermal excitation during the testing often leads to uneven heat distribution within the original thermal images, and the limited heat absorption by the surface of specimen may cause low-contrast defect signals that further impair the identification of defects. In response to these challenges, this study introduces an enhancement framework, termed Reflectance-Correction Retinex (RCR). In the RCR framework, the single-scale Retinex method is first applied to equalize the non-uniform background within a thermal image, and a sigmoid modification is proposed to adaptively amplify the contrast between defects and their background. The RCR framework is tested on PT images of man-made CFRP specimens. The experimental results show that our proposed method substantially enhances the visual quality of PT images, which facilitates precision and reliability in defect detection applications.

5. Robust Bipartite Output Regulation of Linear Uncertain Multi-Agent Systems Under Observer-Based Protocols Jiashuo Liu, Cui-Qin Ma, Yun-Bo Zhao , and Yu Kang IEEE Trans. Circuits Syst. II 2024 [Abs] [doi] [pdf]

   Robust bipartite output regulation of linear uncertain multi-agent systems is studied over a signed digraph. Since only parts of agents have access to the information of the exosystem, a distributed observer is introduced to estimate the exosystem state for each agent. Then, a distributed control protocol is proposed based on the internal model method and observer for the exosystem. By exploiting matrix analysis and algebraic graph theory, sufficient conditions for achieving robust bipartite output regulation are given. It is shown that the multi-agent system being structurally balanced and the augmented multiagent system having a spanning tree with the exosystem being the root are the communication topology conditions for ensuring robust bipartite output regulation. Finally, the correctness of the results is validated by an example.

6. Multivariate Time-Series Modeling and Forecasting With Parallelized Convolution and Decomposed Sparse-Transformer Shusen Ma, Yun-Bo Zhao , Yu Kang, and Peng Bai IEEE Trans. Artif. Intell. 2024 [Abs] [doi] [pdf]

   Many real-world scenarios require accurate predictions of time series, especially in the case of long sequence timeseries forecasting (LSTF), such as predicting traffic flow and electricity consumption. However, existing time-series prediction models encounter certain limitations. First, they struggle with mapping the multidimensional information present in each time step to high dimensions, resulting in information coupling and increased prediction difficulty. Second, these models fail to effectively decompose the intertwined temporal patterns within the time series, which hinders their ability to learn more predictable features. To overcome these challenges, we propose a novel endto-end LSTF model with parallelized convolution and decomposed sparse-Transformer (PCDformer). PCDformer achieves the decoupling of input sequences by parallelizing the convolutional layers, enabling the simultaneous processing of different variables within the input sequence. To decompose distinct temporal patterns, PCDformer incorporates a temporal decomposition module within the encoder–decoder structure, effectively separating the input sequence into predictable seasonal and trend components. Additionally, to capture the correlation between variables and mitigate the impact of irrelevant information, PCDformer utilizes a sparse self-attention mechanism. Extensive experimentation conducted on five diverse datasets demonstrates the superior performance of PCDformer in LSTF tasks compared to existing approaches, particularly outperforming encoder–decoderbased models.

7. Unified Numerical Predictor-Corrector Guidance Based on Characteristic Model Bin Meng, Hangning Zhang, and Yun-Bo Zhao Chinese Space Science and Technology 2024 [Abs] [doi] [pdf]

   Aerocapture is one of the key technologies for low-cost transportation, with high demands of autonomy, accuracy, and robustness of guidance and control, due to its high reliability requirements for only one chance of trying.A unified numerical predictor-corrector guidance method based on characteristic models for aerocapture is proposed.The numerical predictor-corrector guidance method is used to achieve autonomy and high accuracy, and the characteristic model control method is introduced to achieve robustness.At the same time, by transforming path constraints, characteristic model equations including apogee deviation and altitude differentiation are established.Based on the characteristic model equations, a unified guidance law which can satisfy path constraints and guidance objectives simultaneously is designed.In guidance problems, guidance deviation is not directly obtained from the output of the dynamics at present, but is calculated through integral and algebraic equations.Therefore, the method of directly discretizing differential equations cannot be used to establish characteristic models, which brings great difficulty to characteristic modeling.A method for characteristic modeling of guidance problems is proposed, and convergence analysis of the proposed guidance law is also provided.Finally, a joint numerical simulation of guidance and control considering navigation deviation and various uncertainties is conducted to verify the effectiveness of the proposed method.The proposed unified method can be extended to general aerodynamic entry guidance designs, providing theoretical and methodological support for them.

8. Efficient Bayesian CNN Model Compression Using Bayes by Backprop and L1-Norm Regularization Ali Muhammad Shaikh, Yun-bo Zhao, Aakash Kumar, Munawar Ali, and Yu Kang Neural Process Lett 2024 [Abs] [doi] [pdf]

   The swift advancement of convolutional neural networks (CNNs) in numerous real-world utilizations urges an elevation in computational cost along with the size of the model. In this context, many researchers steered their focus to eradicate these specific issues by compressing the original CNN models by pruning weights and filters, respectively. As filter pruning has an upper hand over the weight pruning method because filter pruning methods don’t impact sparse connectivity patterns. In this work, we suggested a Bayesian Convolutional Neural Network (BayesCNN) with Variational Inference, which prefaces probability distribution over weights. For the pruning task of Bayesian CNN, we utilized a combined version of L1-norm with capped L1-norm to help epitomize the amount of information that can be extracted through filter and control regularization. In this formation, we pruned unimportant filters directly without any test accuracy loss and achieved a slimmer model with comparative accuracy. The whole process of pruning is iterative and to validate the performance of our proposed work, we utilized several different CNN architectures on the standard classification dataset available. We have compared our results with non-Bayesian CNN models particularly, datasets such as CIFAR-10 on VGG-16, and pruned 75.8% parameters with float-point-operations (FLOPs) reduction of 51.3% without loss of accuracy and has achieved advancement in state-of-art.

9. TGSYOLO: Template-Guidance Siamese Network for SMT Welding Defect Detection Kehao Shi, Chengkai Yu, Yang Cao, Yu Kang, Yunbo Zhao, Lijun Zhao, and Zhenyi Xu IEEE Transactions on Components, Packaging and Manufacturing Technology 2024 [Abs] [doi] [pdf]

   Surface-mounted technology (SMT) welding defect detection plays a key role in the printed circuit board assembly (PCBA) production process, which affects the use of electronic products and cost. Previous works tend to realize defect detection with only defect samples and they assume that there are sufficient defect samples. However, defect samples are usually difficult to collect in real-life scenarios while enough template samples can be easily obtained. In addition, most existing works carry out defect detection based on benchmarks with simple backgrounds of PCBA, which is not suitable for PCBA with complex structures in modern electronic product manufacturing. To address the above issues, we propose a template-guidance Siamese network based on YOLO for SMT welding defect detection (TGSYOLO), which is deployed on a real SMT automatic optical inspection (AOI) system. First, the two-stream structure is introduced to extract deep features in defect images and template images, in which template features serve as guidance knowledge. Then, a template fusion Transformer (TFT) is proposed to model global features between detect and template features in the low-level stage, which could acquire long-range correlations to force the network to focus on potential defect regions. Next, to avoid the disappearance of tiny defect features during deep feature fusion, a multiscale attention feature pyramid network (MAFPN) is proposed to directly fuse defect semantic information from low-level features, which retains detailed expressions of defects through skip connection and obtains compact fusion features. Furthermore, we collect limited welding defect samples based on more complex PCBA backgrounds than previous works through a real SMT AOI system. Experiments on the limited dataset show that TGSYOLO could reach 0.985 of mAP@0.5, 0.885 of mAP@0.75, and 0.984 of F1, which is 0.008, 0.054, and 0.025 higher than other SOTA methods. Also, generalization experiments on the public DeepPCB show that TGSYOLO could still reach the best with 0.991 of mAP@0.5 and 0.89 of mAP@0.75, which proves that TGSYOLO has good generalization performance.

10. 对手类型未知情况下的两人零和马尔科夫博弈决策 王成意, 朱进, and 赵云波 控制理论与应用 2024 [Abs] [doi] [pdf]

    本文研究一类典型的非完全信息博弈问题—–对手类型未知的两人零和马尔科夫博弈, 其中对手类型多样 且每次博弈开始前无法得知对手类型. 文中提出了一种基于模型的多智能体强化学习算法—–对手辨识的极大极 小Q学习(DOMQ). 该算法首先建立对手相关环境的经验模型, 再使用经验模型学习纳什均衡策略, 己方智能体在实 际博弈中根据经验模型判断对手类型, 从而使用相应的纳什均衡策略, 以保证收益下限. 本文所提的DOMQ算法只 需要在采样阶段的每轮博弈结束后得知对手的类型, 除此之外无需知道任何环境的信息. 仿真实验验证了所提算法 的有效性.

11. Rolling Self-Triggered Distributed MPC for Dynamically Coupled Nonlinear Systems Tao Wang, Yu Kang, Pengfei Li, Yun-Bo Zhao , and Hao Tang Automatica 2024 [Abs] [doi] [pdf]

    The mutual influences caused by dynamic couplings in large-scale systems increase the difficulty in the design and analysis of distributed model predictive control (DMPC), and require information exchange among subsystems which calls for a scheduling strategy to save communication resources in communication-limited environments. To circumvent the two problems, we design a rolling selftriggered DMPC strategy for large-scale dynamically coupled systems with state and control input constraints. First, the optimal control problem where the cost is subject to the coupled dynamic and the constraints are subject to the uncoupled counterpart is proposed, forming the dual-model DMPC that is simple in design and analysis but yields good control performance. Second, the information exchange only occurs at some specified triggering instants determined by a rolling self-triggered mechanism, saving communication resources more significantly. The effectiveness of the designed strategy is verified by numerical simulations.

12. An Information Fusion Approach Based on Weight Correction and Evidence Theory Xugang Xi, Yaqing Nie, Yu Zhou, Yun-Bo Zhao , Ting Wang, Yahong Chen, Lihua Li, and Jian Yang Journal of Computational Science 2024 [Abs] [doi] [pdf]

    The combination rules of the Dempster-Shafer evidence theory can lead to illogical results for highly conflicting evidence from different information sources. We propose a general formula for conflict degree calculation from the perspective of modifying evidence sources as a weighted sum of conflict coefficients and Jousselme distance, and the new metric of focal element dispersion is defined by adaptively adjusting the ratio of these two metrics: if the focal element dispersion is too high, the impact of conflict coefficients is increased, and vice versa. We then define the concept of preference consistency and propose a formula for calculating this metric that redistributes the weights of individual pieces of evidence based on the preferences of all evidence. Finally, typical examples show that the proposed rules can manage conflicting evidence with better convergence and interference resistance.

13. Prescribed-Time Output Feedback Control for Cyber-Physical Systems Under Output Constraints and Malicious Attacks Yangang Yao, Yu Kang, Yunbo Zhao, Pengfei Li, and Jieqing Tan IEEE Transactions on Cybernetics 2024 [Abs] [doi] [pdf]

    This article presents a prescribed-time output feedback control (PTOFC) algorithm for cyber-physical systems (CPSs) under output constraint occurring in any finite time interval (OC-AFT) and malicious attacks. The OC-AFT meaning that the output constraint only occurs during a finite number of time periods while being absent in others, which is more general and complex than traditional infinite-time/deferred output constraints. A stretch model-based nonlinear mapping function is constructed to handle the OC-AFT, and a salient advantage is that the proposed algorithm is also suit for CPSs with infinite-time/deferred output (or funnel) constraints, as well as those that are constraint-free, without necessitating changes to the control structure. The uncertain terms (including system model uncertainties, malicious attacks, and external disturbances) are compensated by fuzzy logic systems. Furthermore, a novel practical prescribed-time stability criterion is proposed, under which a novel PTOFC scheme is given. The results demonstrate that the proposed scheme can ensure that both tracking error and observation error converge to a neighborhood centered on zero within a prescribed time, while accommodating the OC-AFT and malicious attacks. Additionally, the settling time remains unaffected by control parameters and initial states, and the limitations of excessive initial control inputs and singularity problems in existing prescribed-time control algorithms are eliminated. The developed algorithm is exemplified through simulation instances.

14. Flexible Prescribed Performance Output Feedback Control for Nonlinear Systems With Input Saturation Yangang Yao, Yu Kang, Yunbo Zhao, Pengfei Li, and Jieqing Tan IEEE Trans. Fuzzy Syst. 2024 [Abs] [doi] [pdf]

    A flexible prescribed performance control (FPPC) approach for input saturated nonlinear systems (ISNSs) with unmeasurable states is first presented in this article. Compared to the standard prescribed performance control (SPPC) or funnel control methods for ISNSs, the “flexibility” of the proposed FPPC algorithm is reflected in two aspects: (1) the proposed FPPC algorithm simultaneously considers multiple key indicators (including the steady state accuracy, convergence time and overshoot), which are widely demanded in industrial production; (2) the proposed FPPC algorithm achieves a trade-off between performance constraint and input saturation, i.e., the performance boundary can adaptively increase when the control input exceeds the saturation threshold, effectively avoiding singularity; conversely, when the control input is within the saturation threshold range, the performance constraint boundary can adaptively revert back to the original performance boundary. In addition, the unmeasured states are observed by the state observer, and the unknown nonlinear functions are approximated by fuzzy logic systems (FLSs). The results demonstrate that the proposed output feedback control algorithm can ensure that all closed-loop signals are semi-globally bounded, the system output can track the desired signal within a prescribed time, and the tracking error is consistently maintained within flexible performance boundaries that depend on input and output constraints. The developed algorithm is exemplified through simulation instances.

15. Unified Fuzzy Control of High-Order Nonlinear Systems With Multitype State Constraints Yangang Yao, Yu Kang, Yunbo Zhao, Pengfei Li, and Jieqing Tan IEEE Trans. Cybern. 2024 [Abs] [doi] [pdf]

    This article presents a unified adaptive fuzzy control approach for high-order nonlinear systems (HONSs) with multitype state constraints. Existing methods always require that the upper and lower constraint boundaries are strictly positive and negative functions (or constants) respectively, which is often inconsistent with the actual constraints. In this article, “multitype state constraints” means that the upper and lower constraint boundaries include multiple types, such as both being strictly positive (or negative), sometime be positive or negative and so on (cases ̈-±). By designing a unified mapping function (UMF), the multi-type state constraints are processed under removal the feasibility conditions (FCs). Furthermore, a technical design makes the proposed method also suitable for unconstrained HONSs. By means of fuzzy logic system (FLS) and fixed-time stability theory (FTST), the proposed algorithm can ensure that the tracking error converges to a zero-centered neighborhood within a fixed time. In addition, the adaptive event-triggered control (AETC) technique which can adjust trigger threshold automatically according to tracking error is introduced to save network resources. Simulation results demonstrate the scheme developed.

16. A Novel Prescribed-Time Control Approach of State-Constrained High-Order Nonlinear Systems Yangang Yao, Yu Kang, Yunbo Zhao, Pengfei Li, and Jieqing Tan IEEE Trans. Syst. Man Cybern, Syst. 2024 [Abs] [doi] [pdf]

    A novel practical prescribed-time control (PPTC) approach for high-order nonlinear systems (HONSs) subject to state constraints is studied in this article. Different from the existing methods which always require the constraint boundaries to be continuous functions, the state constraints considered in this article are discontinuous (i.e., the state constraints occur only in some time periods and not in others), which can be found in many practical systems. By designing a novel stretch modelbased nonlinear mapping function (NMF), the state constraints are dealt with directly, and the limitations that the virtual control function depends upon the feasibility condition (FC) and the tracking error depends upon the constraint boundaries in the conventional schemes are removed. Meanwhile, the proposed method is a unified one, which is also effective for HONSs with conventional continuous state constraints/ deferred state constraints/ funnel constraints or constraints-free without altering the control structure. Furthermore, by designing a newly timevarying scaling transformation function (STF), a more relaxed criterion for practical prescribed-time stable (PPTS) is given, based on which a newly PPTC algorithm is designed. The result shows that the proposed algorithm can preset the upper bound of the settling time, which does not depend upon the initial state of the system and control parameters, the limitations of singularity problem and excessive initial control input in existing methods are removed. Simulation examples verify the algorithm developed.

17. Constrained Common Invariant Subspace and Its Application Dongdong Zhao, Yu Kang, Yun-Bo Zhao , Li Xu, and Shi Yan IEEE Transactions on Automatic Control 2024 [Abs] [doi] [pdf]

    The notion of constrained common invariant subspaces (CCISs) is proposed in this article as a generalization of the well-known invariant subspace to study the structural properties of multiple matrices. Specifically, some necessary and sufficient conditions for the existence of a CCIS are established to provide a methodology to compute such a CCIS. Then, the properties of CCISs and their relation to common eigenvectors are revealed. The existence of common eigenvectors leads to the existence of CCIS, but not vice versa, so the established CCIS can reveal the structural properties of multiple matrices better than common eigenvectors can. The established CCIS is applied to the reducibility of Fornasini–Marchesini (F-M) state-space models, i.e., the necessary and sufficient conditions and the related algorithm for reducibility of F-M models are developed. Finally, a gain-scheduled state-feedback control is proposed for a rational parameter system to further demonstrate the superiority of the established CCIS.

18. A Health Indicator Enabling Both First Predicting Time Detection and Remaining Useful Life Prediction: Application to Rotating Machinery Yun-Sheng Zhao, Pengfei Li, Yu Kang, and Yun-Bo Zhao Measurement 2024 [Abs] [doi] [pdf]

    Remaining Useful Life (RUL) prediction is vital for system functionality. Non-end-to-end approaches is an important type of RUL prediction approaches for their important application in industrial scenarios. In non-end-to-end approaches, Health Indicator (HI) construction is a critical aspect. However, existing HI construction approaches ignore First Predicting Time (FPT) detection, leading to increased domain knowledge demand and system health comprehension difficulty. To address this issue, this paper proposes a multi-objective-optimization-based HI construction approach enabling both FPT detection and RUL prediction. A novel metric called the monotonicity strength index is proposed to address the limitation of the conventional monotonicity. The constructed HI possesses the ability to indicate FPT by taking the detectability metric as an optimization objective. The optimization problem is solved by the combination of the multi-objective ant lion optimizer and the entropy weight method. The superiority of this HI is demonstrated through experiments on the widely used IMS bearing dataset and a gearbox dataset.

Conference Articles

1. Functional Test-Cost Reduction Based on Optimization Modeling and Congestion Control Peng Bai, Kangcheng Wang, Yun-Bo Zhao , Yu Kang, and Wenhao Fang In 2024 14th Asian Control Conference (ASCC) 2024 [Abs] [pdf]

   Functional testing is a crucial process to guarantee the quality of electronic products. In recent years, the cost of functional testing has been rising with the increasing complexity of products, and reducing testing costs is of great significance to the economic efficiency of electronic manufacturing enterprises. Related research has not yet fully considered the issue of the nonuniform distribution of functional testing samples in practical applications, making it challenging to ensure the effectiveness of reducing testing costs. Inspired by the concept of TCP congestion control algorithms, this paper presents an enhanced congestion control algorithm tailored for the functional testing process and proposes a method to reduce testing costs accordingly. The proposed method can design dynamically changing testing strategies based on optimal modeling. On the simulation data closely resembles the actual data, the proposed method can significantly reduce the testing costs compared to the pure optimization modeling method.

2. A Reliable Ensemble Model Based on Hierarchical Component Features for Repair Label Prediction of Soldering Defects Longxin Chen, Yunbo Zhao, Binkun Liu, Shaojie Dong, Huijuan Zhu, and Peng Bai In 2024 14th Asian Control Conference (ASCC) 2024 [Abs] [pdf]

   Using solder paste inspection (SPI) and automated optical inspection (AOI) data, accurate prediction for repair labels of soldering defective printed circuit board (PCB) components can help reduce labor costs. Existing research tries to pick out both the false defect components (actually good) and impossible-to-repair components among defective PCB components, using SPI and AOI data. However, it is inappropriate to pick out the false defect components from screened components using defective information in AOI data. Therefore, the problem setting of existing research is inappropriate, resulting in the algorithm’s performance not meeting actual requirements. To address this problem, we only care about the reliable prediction of impossible-to-repair components. We propose a hierarchical component feature extraction method that can comprehensively characterize the degree of component defects from multiple levels, including pin level and component level. Then we apply the ensemble model based on XGBoost and TabNet and adjust the probability threshold of components judged as impossible-to-repair category, achieving the reliable prediction of impossible-to-repair components. Finally, we validated our method on real datasets and achieved better experimental results compared to baseline methods, which can meet actual requirements,

3. An Authority Allocation Strategy for Shared Control in Human-Machine Cooperative Driving via Lane-Based Probabilistic Collision Risk Assessment Xiuhua Liang, Yunbo Zhao, Yu Kang, and Chang Xu In 2024 14th Asian Control Conference (ASCC) 2024 [Abs] [pdf]

   An effective shared control strategy plays a crucial role in assisting drivers during hazardous situations in human-machine cooperative driving. This study introduces an author-ity allocation strategy for shared control based on collision risk assessment in a long-term maneuver. Initially, maneuvers are categorized by lanes, which can properly represent lane-based driving characteristics in real-world driving conditions. Subsequently, multiple lane models are built to combine with interactive multiple models to compute target lane probabilities. The target lane probabilities indicate the likelihood of a vehi-cle moving toward or remaining in each lane, determined by its lateral position in curvilinear coordinates. Finally, collision risk is evaluated through the integration of model probability distribution of lanes and artificial potential field value between a pair of predicted trajectories. Intuitively, the driver’s authority is directly correlated with the collision risk, so authorities between the driver and the machine are determined by the collision risk through a piecewise proportional function. The effectiveness of the proposed algorithm is verified using the Carla simulator in a cut-in scenario.

4. FMIP: Feature Map Importance Pruning for Efficient CNN Optimization Ali Muhammad Shaikh, Yun-bo Zhao, Yu Kang, and Aakash Kumar In 7th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI 2024) 2024 [Abs] [pdf]

   Deep convolutional neural networks (CNNs) have reformed numerous fields, comprising computer vision and natural language processing. Nevertheless, CNNs’ computational complexity frequently raises obstacles to utilization on resourceconstrained devices. Different optimization strategies have been proposed in response to this issue, comprising quantization, knowledge distillation, and pruning. This paper familiarizes a novel pruning method, Feature Map Importance Pruning (FMIP), designed to optimize the performance of deep CNNs while reducing computational costs. The FMIP computes the importance of feature maps within convolutional layers according to the area of their activation values, facilitating a systematic approach to pruning decisions. By detecting and eliminating the redundant feature maps, FMIP can considerably cut the number of parameters and FLOPs in a CNN model without conceding accuracy. This is chiefly advantageous for using CNNs on devices with limited memory and computational power.

5. Uncertainty-Based Dynamic Weighted Experience Replay for Human-in-the-Loop Deep Reinforcement Learning Xia Tian, Yu Kang, Yun-Bo Zhao , Ya-Qing Zhou, and Peng-Fei Li In 2024 [Abs] [pdf]

   Human-in-the-loop reinforcement learning (HIRL) enhances sampling efficiency in deep reinforcement learning by incorporating human expertise and experience into the training process. However, HIRL methods still heavily depend on expert guidance, which is a key factor limiting their further development and largescale application. In this paper, an uncertainty-based dynamic weighted experience replay approach (UDWER) is proposed to solve the above problem. Our approach enables the algorithm to detect decision uncertainty, triggering human intervention only when uncertainty exceeds a threshold. This reduces the need for continuous human supervision. Additionally, we design a dynamic experience replay mechanism that prioritizes machine self-exploration and humanguided samples with different weights based on decision uncertainty. We also provide a theoretical derivation and related discussion. Experiments in the Lunar Lander environment demonstrate improved sampling efficiency and reduced reliance on human guidance.

6. Trust-Modulated Authority Allocation in Human-Guided Goal Recognition Tasks Rui-Yu Xia, Yun-Bo Zhao , Jun-Sen Lu, Yang Wang, Peng-Fei Li, and Yu Kang In 2024 [Abs] [pdf]

   In shared control teleoperation, the machine infers the humans’ goal to provide effective assistance, which we call human-guided goal recognition. However, current methods mainly use algorithm confidence to assign control authority during the process, which makes it difficult to correct machine inference errors under high confidence. To address this problem, we propose a trust model that considers machine capability fluctuations and human-machine interaction experience. We also add trust as a dynamic assessment of machine capabilities to authority allocation to improve the success rate of the tasks. Finally, we verify the effectiveness of the proposed method through experiments.

7. A Novel Haptic Takeover Method Based on Human-Machine Collaboration States Yunbo Zhao, Zuhao Xie, Chang Xu, Xiuhua Liang, Ruiyu Xia, and Jiayu Li In 2024 14th Asian Control Conference (ASCC) 2024 [Abs] [pdf]

   The current stage of autonomous driving calls for drivers to remain actively engaged within the control loop in anticipation of the need for takeover operations. However, regaining control of the vehicle from a state of low situation awareness (SA) poses a challenge. To address this issue, this research introduces a novel takeover method based on haptic shared control, ensuring a smooth and safe takeover process. A symmetric softmax function is formulated to evaluate muscle state, taking into account the varying torque thresholds associated with different vehicle speeds, as well as utilizing the driver’s cognitive state to characterize their SA. Within the takeover process, a coordinator leverages the driver’s SA and human-machine intention similarity to determine the current state of human-machine collaboration. Subsequently, different control allocation strategies are then adopted for different states, and the driver is guided through force feedback. Experiment results demonstrate the effectiveness of the proposed method, showcasing its ability to facilitate a smooth and safe transfer of control, regardless of the presence of conflicts or the harmonious state existing between the human driver and the automated system.

8. Prediction of Yield in Functional Testing of Motherboards in Laptop Manufacturing Yunbo Zhao, Shaojie Dong, Yu Kang, Kangcheng Wang, Longxin Chen, and Peng Bai In 2024 14th Asian Control Conference (ASCC) 2024 [Abs] [pdf]

   Functional testing stands as a pivotal quality control step in the production process of laptop motherboards, aiming to validate the proper functioning of various components. However, due to the multitude of functional modules involved on the motherboard, testing all of them requires a significant amount of time and resources. As a result, production line engineers often rely on empirical selection of modules with low yield rates for testing. However, such empirical yield estimation is often inaccurate. To address this challenge, this study proposes a hybrid model based on XGBoost and Long Short-Term Memory (LSTM) networks to predict the yield of each functional module. By harnessing the feature learning capability of XGBoost and the sequential modeling power of LSTM, this model efficiently explores the intricate correlations among motherboard functional modules, thereby accurately forecasting their yields. We extensively train and validate the model using historical production data and successfully deploy it on real laptop motherboard production lines. Experimental results demonstrate that our hybrid model accurately predicts the yield of each functional module, providing crucial guidance for the functional testing process. Through in-depth analysis of the predicted yield results, engineers can systematically choose testing projects to save time and resources. This research offers a novel approach and pathway for enhancing motherboard production efficiency and quality.

9. A Real-time Detection Method for SMT Chip Component Defects Based on Adaptive Collaborative Feature Yunbo Zhao, Wangyou Gui, Yu Kang, Kehao Shi, Lijun Zhao, and Zhenyi Xu In 2024 International Conference on Guidance, Navigation and Control (ICGNC 2024) 2024 [Abs] [pdf]

   Surface Mounted Technology (SMT) is an electronic assembly process that involves the placement of electronic components on a printed circuit board. In the SMT process, defect detection technology is the key to controlling the quality of electronic products. In the Industry, AOI technology based on image processing is widespread. However, it is plagued by several challenges including slow response time and a high defect mis-detection rate, warranting the need for further research and advancement. In recent years, researchers have turned to deep learning-based target detection algorithms for industrial defect detection. Nevertheless, in scenarios such as SMT, complexities arising from intricate object shapes and the challenge of balancing accuracy and speed present significant hurdles, rendering common target detection algorithms inadequate for meeting the demands of these scenarios. To solve these problems, this paper proposes a real-time detection method for SMT chip component defects based on adaptive collaborative feature (SMT-DETR), which employs an adaptive collaborative feature extraction module ACBlock to the deformate features of the object, and can pay attention to the defective changes of the chip components effectively. Secondly, a new IMIoU loss is proposed in this paper, which can capture the tiny object information more effectively and has faster convergence speed by combining the efficient IoU loss function. Finally, experiments show that the proposed method is better compared to classical object detection algorithms.

10. Functional Test-Cost Reduction Based on Fault Tree Analysis and Binary Optimization Xiaojie Zuo, Kangcheng Wang, Yun-Bo Zhao , Yu Kang, and Peng Bai In 2024 43rd Chinese Control Conference (CCC) 2024 [Abs] [doi] [pdf]

    With the rapid increase in the complexity of electronics, the cost of the functional testing process used to ensure product functionality continues to rise. Optimization modeling based on reliability analysis is an effective approach to reduce testing costs. However, the reliability calculations of existing methods often exhibit significant deviations, making it challenging to guarantee the effectiveness of the resulting testing strategies in practical applications. To address this issue, this article proposes an optimization modeling method that integrates statistical analysis and reliability analysis. The expression for the reliability of the system’s key stage is formulated by analyzing the system’s reliability. Statistical analysis is utilized to exploit the inherent reliability information in the enormous process data to determine the probability of the root causes of system failures. The reliability of the key stage is quantitatively calculated based on the structure of the fault tree tailored for the system. On this basis, a binary optimization model is established to obtain a testing strategy with strong generalization ability and reduce the cost of functional testing. The effectiveness of the proposed method is verified through a simulation case study.

2023

Conference Articles

1. A Human-Machine Trust Model Integrating Machine Estimated Performance Shaojun Chen, Yun-Bo Zhao , Yang Wang, and Junsen Lu In 2023 6th International Symposium on Autonomous Systems (ISAS) 2023 [Abs] [doi] [pdf]

   The prediction of human trust in machines within decision-aid systems is crucial for improving system performance. However, previous studies have only measured machine performance based on its decision history, failing to account for the machine’s current decision state. This delay in evaluating machine performance can result in biased trust predictions, making it challenging to enhance the overall performance of the human-machine system. To address this issue, this paper proposes incorporating machine estimated performance scores into a human-machine trust prediction model to improve trust prediction accuracy and system performance. We also provide an explanation for how this model can enhance system performance. To estimate the accuracy of the machine’s current decision, we employ the KNN method and obtain a corresponding performance score. Next, we report the estimated score to humans through the human-machine interaction interface and obtain human trust via trust self-reporting. Finally, we fit the trust prediction model parameters using data and evaluate the model’s efficacy through simulation on a public dataset. Our ablation experiments show that the model reduces trust prediction bias by 3.6% and significantly enhances the overall accuracy of humanmachine decision-making.

2. Spectrally Normalized Adaptive Neural Identifier for Dynamic Modeling and Trajectory Tracking Control of Unmanned Aerial Vehicle Shaofeng Chen, Yu Kang, Yunbo Zhao, and Yang Cao In Advances in Guidance, Navigation and Control 2023 [Abs] [doi] [pdf]

   Accurate dynamic modeling is difficult for aerobatic unmanned aerial vehicles flying at their physical limit, due to the model uncertainty caused by unobservable hidden states like airflow and vibrations. Although some progresses have been made, these hidden states are still not properly characterized, rendering system identification problem for aerobatic unmanned aerial vehicle extremely challenging. To address this issue, a novel spectrally normalized adaptive neural identifier is proposed for the dynamic modeling of aerobatic unmanned aerial vehicles. Specifically, to characterize the model uncertainty, we propose a spectrally normalized adaptive neural network (SNANet) to extract deep features representing the hidden states of the system. Particularly, the proposed SNANet adopts a multi-model adaptive structure, quickly and dynamically updating the model online. Furthermore, the spectral normalization constraint is introduced into the training process to ensure the Lipschitz stability of the SNANet. Consequently, a trajectory tracking control scheme including the sliding mode controller and SNANet is presented. The modeling effectiveness of the proposed method is verified on a real flight dataset. The results demonstrate that our method has high modeling accuracy, short training time, and fast model response speed.

3. Board-Level Functional Test Selection Based on Fault Tree Analysis Yaoyao Li, Kangcheng Wang, Yu Kang, Yunbo Zhao, and Peng Bai In 2023 6th International Symposium on Autonomous Systems (ISAS) 2023 [Abs] [doi] [pdf]

   With the increasing complexity of the circuit board, the cost of board-level functional test ensuring the board quality becomes dramatically high. Data-driven-based test selection methods have been widely studied for test-cost reduction. However, existing test selection methods tend to overfit due to overlooking the root causes of faulty boards. To address this issue, a test selection method based on reliability analysis is proposed. A fault tree oriented to the board-level functional test is established for analyzing the reliability of the board and test items. The reliability analysis result is then effectively utilized to formulate a test selection method. Three indices are introduced to evaluate the test efficiency and the test quality. Experimental results demonstrate the effectiveness of the proposed method.

4. A Feature Engineering-based Method for PCB Solder Paste Position Offset Prediction Binkun Liu, Yunbo Zhao, Yu Kang, Yang Cao, Peng Bai, and Zhenyi Xu In 2023 6th International Symposium on Autonomous Systems (ISAS) 2023 [Abs] [doi] [pdf]

   Solder paste printing position offset is a common type of defective printed circuit board (PCB) printing, and accurate position offset prediction helps to avoid the production of defects, thus improving efficiency. The existing methods mainly use the powerful nonlinear fitting ability of deep learning to learn the variation pattern of solder paste printing quality to achieve a good prediction. However, factories also focus on the interpretability of the model, and existing methods are difficult to give the basis for decisions, so there are still limitations in the practical application. To solve this problem, we propose a Support vector machine (SVM) approach, in which we manually design 14 statistical features based on the original data, then the resampling reduces the effect of data imbalance and achieves PCB pad-level offset prediction. Finally we verified on about one week of real solder paste printing production data and achieved good experimental results.

5. Robust Optimization Based Air Combat Game Decision-Making of Multi-Uav with Uncertain Information Haotian Liu, Yuhui Wang, Shouyi Li, Lei Sun, Yu Kang, and Yunbo Zhao In 2023 5th International Conference on Industrial Artificial Intelligence (IAI) 2023 [Abs] [doi] [pdf]

   The objective of this study is to design a game decision-making method for the air combat of multiple unmanned air vehicles (multi-UAV) with uncertain information, which is based on flexible and robust optimization. Aiming at the problem that the Nash equilibrium of the UAVs air combat game under uncertain information is not easy to solve, this paper proposes a new method for solving the Nash equilibrium of the air combat game under uncertain information. First of all, the interval payoff matrices of both sides are obtained according to the air combat superiority evaluation method. Secondly, the theory of obtaining the Nash equilibrium of a game by solving a linear programming problem is reviewed. Based on this idea, a novel method is proposed for solving the air combat game problem of multi-UAV with uncertain information, which is achieved by transforming it into a linear programming problem with uncertain parameters. Finally, the effectiveness of the method is verified by simulation, which shows that our method can provide new tools for solving problems in air combat with Uncertain Information.

6. Swap Softmax Twin Delayed Deep Deterministic Policy Gradient Chaohu Liu, and Yunbo Zhao In 2023 6th International Symposium on Autonomous Systems (ISAS) 2023 [Abs] [doi] [pdf]

   Reinforcement learning algorithms have achieved remarkable success in the realm of continuous control. Among the extensively used algorithms, the Deep Deterministic Policy Gradient algorithm (DDPG) is one of the classic continuous control algorithms, which is prone to the problem of overestimation. Subsequently, the Twin Delayed Deep Deterministic Policy Gradient algorithm (TD3) was proposed, which incorporated the idea of double DQN by taking the minimum value between a pair of critics in order to limit overestimation. Nevertheless, TD3 may lead to an underestimation bias. In order to reduce the effect of errors, we introduce a new method by incorporating Swap Softmax to TD3, which can offset the maximum and minimum values. We evaluate our method on continuous control tasks from OpenAI Gym simulated by MuJoCo and the results show that it has an improvement in performance and robustness.

7. Game Decision of Multi-UAV Based on Improved Shark Smell Optimization Algorithm Lei Sun, Yuhui Wang, Tongle Zhou, Yu Kang, and Yun-Bo Zhao In Advances in Guidance, Navigation and Control 2023 [Abs] [doi] [pdf]

   Decision-making is one of the key technologies in the air combat field. In this work, a game decision method based on an improved shark smell optimization (SSO) algorithm is developed for unmanned aerial vehicles (UAVs). The air combat situation assessment result of multi-UAV is described as an uncertain set, and a game model of game decision is established. Then, to upgrade the efficiency of game decision, an improved \\θ\\θ-SSO algorithm is proposed. Finally, the simulation results turn out the effectiveness of the algorithm.

8. Delay-Based Feedback Formation Control for Unmanned Aerial Vehicles with Feedforward Components Li Wang, Yan-Dong Zhao, Bao-Lin Zhang, Zhihui Cai, Jian Xue, and Yun-Bo Zhao In Advances in Guidance, Navigation and Control 2023 [Abs] [doi] [pdf]

   This paper mainly studies the delay-based feedback formation control problem with feedforward components for multiple unmanned aerial vehicles (UAVs) system. First, the kinematic equation of the leader-follower UAVs formation system with regard to three directions is established, and the communication network topology between UAVs is presented. Second, by intentionally introducing time-delay into feedback control channel, a delay-based feedback formation control scheme with feedforward components is proposed for the multiple UAVs system. The sufficient conditions of asymptotical stability of closed-loop system are derived based on the linear matrix inequality (LMI) theory, and the design method of the delayed formation controller is presented. The effectiveness of this control scheme is verified based on simulation results, which show that under the designed formation controller, the formation performance of the multiple UAVs system can be guaranteed effectively.

9. Defect Detection of Laptop Appearance Based on Improved Multi-Scale Normalizing Flows Jie Zhang, Zerui Li, and Yunbo Zhao In 2023 38th Youth Academic Annual Conference of Chinese Association of Automation (YAC) 2023 [Abs] [doi] [pdf]

   In the laptop production process, timely detection of appearance defects is essential to ensure product quality. At present, there are many shortcomings in the manual visual inspection-based method on the laptops production line. In addition, due to the wide variety of laptop appearance defects and extreme differences in defect scales, existing defect detection algorithms perform poorly in the field of laptop appearance inspection. In response to the above problems, this paper proposes a defect detection algorithm based on improved multi-scale normalizing flows. First, the multi-level features extracted from the backbone network are fused by using the pyramid feature fusion module to obtain multi-scale features with rich semantic and spatial information. Then, the effective density estimation of the multi-scale features is achieved by fusing the normalizing flows of attention mechanisms. Finally, the defects are detected and localized based on the output likelihood values. The experimental results demonstrate the effectiveness of the proposed method in detecting and locating appearance defects.

10. Global Finite-Time Stabilization for a Class of Switched Nonhomogeneous Positive Systems Via Impulsive Control Niankun Zhang, Yu Kang, and Yunbo Zhao In 2023 6th International Symposium on Autonomous Systems (ISAS) 2023 [Abs] [doi] [pdf]

    This paper studies the global finite-time stabilization problem for a type of switched nonhomogeneous positive nonlinear systems using impulsive control. Based on positive system theory and impulsive control technology, we introduce suitable impulsive inputs at switching points and design a class of stabilizing impulses coupled with maximum dwell-time (MDT) conditions, under which the considered system is globally finitetime stable. Meanwhile, the upper bound of settling-time function can be derived under MDT conditions, which uncovers the relationship among impulse intensity, initial value and control frequency. Finally, an example is presented to verify our result.

Journal Articles

1. 基于动态信道切换的无线网络化控制系统的资源调度策略 郝小梅, and 赵云波 高技术通讯 2023 [Abs] [pdf]

   本文针对通信网络中存在竞争和非竞争信道的无线网络化控制系统，提出了一种基于估计 器的信道选择策略，在保证控制系统稳定性的同时尽可能地节约了宝贵的非竞争信道资源。在无 线网络化控制系统中，控制信号通过竞争信道传输时可能发生数据包丢失，导致执行器无法收到 实时的控制信号。而传感器端未知控制信号的实际传输情况，因而也无法得知每个时刻执行器所 使用的控制信号。针对这种情况，本文首先设计了估计器来估计执行器端上一时刻实际使用的控 制信号，再通过信道选择策略来约束执行器端使用控制信号的误差。然后，在所提信道选择策略 下设计控制器来保证控制系统稳定。最后，通过数值仿真验证了所提算法的有效性。

2. Compound Event-Triggered Distributed MPC for Coupled Nonlinear Systems Yu Kang, Tao Wang, Pengfei Li, Zhenyi Xu, and Yun-Bo Zhao IEEE Trans. Cybern. 2023 [Abs] [doi] [pdf]

   This paper investigates the event-triggered distributed model predictive control (DMPC) for perturbed coupled nonlinear systems subject to state and control input constraints. A novel compound event-triggered DMPC strategy, including a compound triggering condition and a new constraint tightening approach, is developed. In this event-triggered strategy, two stability-related conditions are checked in a parallel manner, which relaxes the requirement of the decrease of the Lyapunov function. As a result, the number of triggering instants can be reduced significantly. Furthermore, the proposed constraint tightening approach solves the problem of the state constraint satisfaction, which is quite challenging due to the external disturbances and the mutual influences caused by dynamical coupling. Simulations are conducted at last to validate the effectiveness of the proposed algorithm.

3. Robust Nonsingular Fixed Time Terminal Sliding Mode Control for Atmospheric Pollution Detection Lidar Scanning Mechanism Yu Kang, Yuxiao Yang, Cai Chen, Wenjun Lü, and Yunbo Zhao J Syst Sci Complex 2023 [Abs] [doi] [pdf]

   A robust nonsingular fixed time terminal sliding mode control scheme with a time delay disturbance observer is proposed for atmospheric pollution detection lidar scanning mechanism (APDL-SM) system. Distinguished from the conventional terminal sliding mode control methods, the authors design a novel fixed-time terminal sliding surface, the convergence time of sliding mode phase of which has a constant upper bound that is designable by adjusting only one parameter. Moreover, in order to overcome the problem of unknown upper bound of lumped uncertainty including model uncertainty, friction effect and external disturbances from the port environment, the authors propose a time delay disturbance observer to provide an estimation for the system lumped uncertainty. By using the Lyapunov synthesis, the explicit analysis of the convergence time upper bound are performed. Finally, simulation studies are conducted on the APDL-SM system to show the fast convergence rate and strong robustness of the proposed control scheme.

4. Disturbance Prediction-Based Adaptive Event-Triggered Model Predictive Control for Perturbed Nonlinear Systems Pengfei Li, Yu Kang, Tao Wang, and Yun-Bo Zhao IEEE Trans. Automat. Contr. 2023 [Abs] [doi] [pdf]

   A disturbance prediction based adaptive event-triggered model predictive control scheme is proposed for nonlinear systems in the presence of slowly varying disturbance. The optimal control problem in the model predictive control scheme is formulated by taking advantage of a proposed central path-based disturbance prediction approach, and the event-triggered mechanism is designed to be adaptive to the triggering interval. As a result, the proposed scheme improves the state prediction precision and hence reduces greatly the triggering frequency. Furthermore, for input-affine nonlinear systems, the disturbance separation and compensation techniques are developed to further enlarge the triggering interval. Theoretical analysis of the algorithm feasibility and closed-loop stability, as well as numerical evaluations of the effectiveness of the proposed schemes, are also given.

5. 具有领导者的高阶线性多运动体系统的群智汇集趋同 马翠芹, 杜梅, and 赵云波 中国科学: 技术科学 2023 [Abs] [doi] [pdf]

   本文研究了具有领导者的高阶线性多运动体系统的群智汇集趋同问题. 利用运动体与其邻居的信息, 分别 为跟随者设计了状态反馈型和输出反馈型控制协议, 并利用矩阵Riccati代数方程、矩阵分析等工具, 给出了系统 实现领导-跟随者群智汇集趋同的充分条件. 研究表明, 当领导者和跟随者所组成的多运动体系统的通信拓扑交 互平衡并且存在一棵生成树时, 只要合理地选取满足条件的控制增益, 系统在所给出的控制协议作用下可以实现 领导-跟随者群智汇集趋同. 特别地, 当为跟随者设计输出反馈型控制协议时, 借助误差系统可以将领导-跟随者群 智汇集趋同问题转化为静态输出反馈问题. 当系统的输入输出矩阵满足一定的秩条件时, 系统在所设计的输出反 馈型控制协议作用下可以实现领导-跟随者群智汇集趋同.

6. Leader-Following Cluster Consensus of Multiagent Systems With Measurement Noise and Weighted Cooperative–Competitive Networks Cui-Qin Ma, Tian-Ya Liu, Yu Kang, and Yun-Bo Zhao IEEE Trans. Syst. Man Cybern, Syst. 2023 [Abs] [doi] [pdf]

   Leader-following cluster consensus is investigated for multi-agent systems with weighted cooperative-competitive networks and measurement noise. A stochastic approximation protocol is proposed for interactively balanced and sub-balanced networks, and pinning control is introduced to deal with the divergence phenomenon in interactively unbalanced networks. With these protocols, sufficient conditions for reaching strong mean square leader-following cluster consensus are established for all the three types of networks, which are also extended to the cases without measurement noise. Numerical examples illustrate the effectiveness of the proposed protocols and theoretical analysis.

7. TCLN: A Transformer-Based Conv-LSTM Network for Multivariate Time Series Forecasting Shusen Ma, Tianhao Zhang, Yun-Bo Zhao , Yu Kang, and Peng Bai Appl Intell 2023 [Abs] [doi] [pdf]

   The study of multivariate time series forecasting (MTSF) problems has high a significance in many areas, such as industrial forecasting and traffic flow forecastm ing. Traditional forecasting models pay more attention to the temporal features of variables and lack depth in extracting spatial and spatiotemporal features between variables. In this paper, a novel model based on the Transformer, convolutional neural network (CNN), and long short-term memory (LSTM) network is proposed to address the issues. The model first extracts the spatial feature vectors through the proposed Multi-kernel CNN. Then it fully extracts the temporal d information by the Encoder layer that consists of the Transformer encoder layer and the LSTM network, which can also obtain the potential spatiotemporal correlation. To extract more feature information, we stack multiple Encoder layers. e Finally, the output is decoded by the Decoder layer composed of the ReLU activat tion function and the Linear layer. To further improve the model’s robustness, we also integrate an autoregressive model. In model evaluation, the proposed model p achieves significant performance improvements over the current benchmark methods for MTSF tasks on four datasets. Further experiments demonstrate that the e model can be used for long-horizon forecasting and achieve satisfactory results on the yield forecasting of test items (our private dataset, TIOB).

8. 基于丢包率估计的无线网络化控制系统的逼近控制策略 吴芳, 梁启鹏, 叶睿卿, and 赵云波 高技术通讯 2023 [Abs] [pdf]

   本文针对丢包为分段伯努利过程的无线网络化控制系统进行了控制器设计和稳定性分析。丢包满足分段伯努利过程是指未知丢包率将在未知时刻突变到另一个未知概率上并保持一段时间。针对这一丢包特点,本文提出了基于丢包率估计的逼近控制策略以保证系统稳定性。首先设计了丢包率估计器和逼近控制器,使系统在线估计丢包率,并利用丢包率估计得到控制量。然后为平衡系统性能和网络信道资源设计了信道调度机制。最后设计了丢包率突变检测器使系统自适应丢包率突变。在此基础上得到了保证闭环系统均方最终一致有界的充分条件和控制增益计算方法。数值仿真验证了控制策略的有效性。

9. 基于轨迹预测与改进人工势场法的机械臂动态避障规划方法 吴芳, and 赵云波 高技术通讯 2023 [Abs] [pdf]

   在人机共存环境中,人可能会成为机器人执行任务过程中的动态障碍物,因此, 在机器人的运动过程中需要动态地避障规划,从而避免机器人危害到用户安全。 人工势 场法是常用的动态路径规划算法,具有实现简单、计算实时性高等优点。 传统的人工势场 法根据虚拟的引力场和斥力场得到合力,从而引导机器人的运动,但是当引力和斥力等大 反向时,存在局部极小值问题。 针对该问题,本文提出了基于平面位置采样的改进人工势 场法。 每次计算得到合力向量后,以该向量的指向为中心,在垂直于地面、包含合力向量 的平面上以特定的角度间隔分别逆时针和顺时针方向采样 90 °范围内的运动方向,然后 分别计算引力值和斥力值,最后根据引力值和斥力值的加权和最小确定机器人的最佳运 动方向。 为了应对用户运动导致机器人运动路径突变的情况,本文依据用户手臂运动和 头部转动的关联关系,通过检测用户的头部姿态,并利用手臂当前的运动信息预测手臂接 下来的运动位置。 最后设计了基于轨迹预测与改进人工势场法的机械臂动态避障规划方 法,实验结果表明,该方法可以有效地进行动态避障,并且规划的路径更加平滑、长度更短。

10. 基于优先级预测器的无线网络化控制系统的动态传输策略 闫文晓, and 赵云波 高技术通讯 2023 [Abs] [doi] [pdf]

    文针对无线通信网络中存在丢包的多包传输无线网络化控制系统，提出了一种基于预测 器的动态传输策略，在几乎不增加信道资源占用的情况下显著提升系统稳定性。在多包传输的无 线网络化控制系统中，由于通信资源的限制，传感器到控制器间的数据传输中出现丢包问题，影 响控制系统性能。针对这个问题，本文首先设计了优先级预测器来预测下一时刻每个传感器数据 对系统稳定性的影响，帮助系统决策每个传感器的发送优先级，再通过传输调节器对不同优先级 传感器补偿相应的随机退避时间上限，进而让优先级高的传感器在随机退避的方式下优先传输， 然后在此策略下设计控制器使系统稳定。最后通过数值仿真验证了本文策略的有效性。

11. 面向人机序贯决策实现共享控制下的仲裁优化 张倩倩, 赵云波, 吕文君, and 陈谋 中国科学：信息科学 2023 [Abs] [doi] [pdf]

    共享控制存在于众多由人类智能和机器智能共同参与的序贯决策场景. 由于人的决策范围和 智能机器的决策范围尚未予以明确划分, 需要加以实时仲裁从而达到人机共存并且共享决策权限. 为 此本文提出了一种仲裁优化方法, 该方法的独特之处在于引入自主性边界概念, 优化了共享控制中人 机决策动作的仲裁机制. 本文为自主性边界的计算和更新维护提供了思路, 能够基于贝叶斯规则的意 图推理分析人机共享系统可能要实现的目标, 从而确定仲裁参数. 此外, 本文还分析了自主性边界的 不确定性以促进边界信息对共享控制中决策质量的优化效果. 实验结果表明, 所提出的方法在累积奖 励、成功率、撞击率方面表现出色, 这些说明了本文提出的共享控制中的仲裁优化方法在求解人机序 贯决策问题时的有效性和价值.

12. Event‐Triggered Asynchronous Filtering for Networked Fuzzy Non‐Homogeneous Markov Jump Systems with Dynamic Quantization Jin Zhu, Zhi Xie, Yun-Bo Zhao , and Geir E. Dullerud Adaptive Control & Signal 2023 [Abs] [doi] [pdf]

    The asynchronous filtering problem for networked fuzzy non-homogeneous Markov jump systems is investigated, with the consideration of packet dropout and quantization. An event-triggered dynamic quantization scheme is proposed, and a stability criterion is given to ensure the stochastic stability of the filtering error systems with desired extended dissipative performance. This performance provides a unified framework in the sense that it can degenerate to H∞, l2 − l∞, dissipativity and passivity filter, respectively, under certain parameter sets. Furthermore, an asynchronous filter design method with extended dissipative performance is given based on the proposed stability criterion. In this method, the existence of the quantizer is ensured by dynamic quantization levels and the solution scope of the filter is enlarged by a free-connection weighting matrices method. The theoretical design and analysis is finally evaluated using a practical example.

2022

Journal Articles

1. 非完全信息下人机合作对抗博弈专题编者按 康宇, 段海滨, and 赵云波 中国科学：信息科学 2022 [doi] [pdf]
2. Event-Based Model Predictive Control for Nonlinear Systems with Dynamic Disturbance Pengfei Li, Tao Wang, Yu Kang, Kun Li, and Yun-Bo Zhao Automatica 2022 [Abs] [doi] [pdf]

   In this paper, we investigate the event-based model predictive control (MPC) for constrained nonlinear systems with dynamic disturbance. An event-triggered disturbance prediction MPC (DPMPC) scheme and a self-triggered counterpart, which explicitly consider the disturbance dynamics, are proposed. For the event-triggered DPMPC scheme, the triggering condition relying on the state prediction error and the predicted disturbance sequence, updates at each time step based on the system states. For the self-triggered DPMPC scheme, the next triggering instant is determined by using the optimal state sequence and predicted disturbance sequence. In both event-based schemes, the optimal control problems are solved only at triggering instants, thus reducing the consumption of computational resources. The effectiveness of the two schemes is demonstrated by a simulation example.

3. Integrated Channel-Aware Scheduling and Packet-Based Predictive Control for Wireless Cloud Control Systems Pengfei Li, Yun-Bo Zhao , and Yu Kang IEEE Trans. Cybern. 2022 [Abs] [doi] [pdf]

   The scheduling and control of wireless cloud control systems involving multiple independent control systems and a centralized cloud computing platform are investigated. For such systems, the scheduling of the data transmission as well as some particular design of the controller can be equally important. From this observation, we propose a dual channel-aware scheduling strategy under the packet-based model predictive control framework, which integrates a decentralized channel-aware access strategy for each sensor, a centralized access strategy for the controllers, and a packet-based predictive controller to stabilize each control system. First, the decentralized scheduling strategy for each sensor is set in a noncooperative game framework and is then designed with asymptotical convergence. Then, the central scheduler for the controllers takes advantage of a prioritized threshold strategy, which outperforms a random one neglecting the information of the channel gains. Finally, we prove the stability for each system by constructing a new Lyapunov function, and further reveal the dependence of the control system stability on the prediction horizon and successful access probabilities of each sensor and controller. These theoretical results are successfully verified by numerical simulation.

4. Cluster Consensus for Coupled Harmonic Oscillators Under a Weighted Cooperative-Competitive Network Cui-Qin Ma, Tian-Ya Liu, and Yun-Bo Zhao International Journal of Control 2022 [Abs] [doi] [pdf]

   Cluster consensus is investigated for multiple coupled harmonic oscillators under a weighted cooperativecompetitive network. Consensus protocols for three categories of communication networks are constructed by employing a weighted gain, and sufficient conditions for guaranteeing cluster consensus are obtained. It is found that under the proposed protocols, the states of all oscillators can be guaranteed to reach periodic orbits that are the same in frequency no matter which cluster the oscillators belong to. In particular, cluster partitions here are not given a prior, but are determined by the communication topology among oscillators. Numerical examples are given to validate the effectiveness of theoretical results.

5. Analysis of Functional Corticomuscular Coupling Based on Multiscale Transfer Spectral Entropy Xugang Xi, Jinsuo Ding, Junhong Wang, Yun-Bo Zhao , Ting Wang, Wanzeng Kong, and Jingqi Li IEEE J. Biomed. Health Inform. 2022 [Abs] [doi] [pdf]

   Functional corticomuscular coupling (FCMC) between the cerebral motor cortex and muscle activity reflects multi-layer and nonlinear interactions in the sensorimotor system. Considering the inherent multiscale characteristics of physiological signals, we proposed multiscale transfer spectral entropy (MSTSE) and introduced the unidirectionally coupled Hénon maps model to verify the effectiveness of MSTSE. We recorded electroencephalogram (EEG) and surface electromyography (sEMG) in steady-state grip tasks of 29 healthy participants and 27 patients. Then, we used MSTSE to analyze the FCMC base on EEG of the bilateral motor areas and the sEMG of the flexor digitorum superficialis (FDS). The results show that MSTSE is superior to transfer spectral entropy (TSE) method in restraining the spurious coupling and detecting the coupling more accurately. The coupling strength was higher in the 1, 2, and 2 bands, among which, it was highest in the 1 band, and reached its maximum at the 22–30 scale. In particular, the coupling strength was higher when the dominant hand or higher grip strengths was used. On the directional characteristics of FCMC, the coupling strength of EEG→sEMG is superior to the opposite direction in most cases. In addition, the coupling strength of the stroke-affected side was lower than that of healthy controls’ right hand in the 1 and 2 bands and the stroke-unaffected side in the 1 band. The coupling strength of the stroke-affected side was higher than that of the stroke-unaffected side and the right hand of healthy controls in the sEMG→EEG direction of 2 band. This study provides a new perspective and lays a foundation for analyzing FCMC and motor dysfunction.

6. 非全时有效人类决策下的人机共享自主方法 游诗艺, 康宇, 赵云波, and 张倩倩 中国科学：信息科学 2022 [Abs] [doi] [pdf]

   在人机共享自主中, 人和智能机器以互补的能力共同完成实时控制任务, 以实现双方单独控制 无法达到的性能. 现有的许多人机共享自主方法倾向于假设人的决策始终“有效”, 即这些决策促进了 任务的完成, 且有效地反映了人类的真实意图. 然而, 在现实中, 由于疲劳、分心等多种原因, 人的决 策会在一定程度上“无效”, 不满足这些方法的基本假设, 导致方法失效, 进而导致任务失败. 在本文 中, 我们提出了一种新的基于深度强化学习的人机共享自主方法, 使系统能够在人类决策长期无效的情况下完成正确的目标. 具体来说, 我们使用深度强化学习训练从系统状态和人类决策到决策价值的 端到端映射, 以显式判断人类决策是否无效. 如果无效, 机器将接管系统以获得更好的性能. 我们将该 方法应用于实时控制任务中, 结果表明该方法能够及时、准确地判断人类决策的有效性, 分配相应的 控制权限, 并最终提高了系统性能.

7. Traded Control of Human–Machine Systems for Sequential Decision-Making Based on Reinforcement Learning Qianqian Zhang, Yu Kang, Yun-Bo Zhao , Pengfei Li, and Shiyi You IEEE Trans. Artif. Intell. 2022 [Abs] [doi] [pdf]

   Sequential decision-making (SDM) is a common type of decision-making problem with sequential and multistage characteristics. Among them, the learning and updating of policy are the main challenges in solving SDM problems. Unlike previous machine autonomy driven by artificial intelligence alone, we improve the control performance of SDM tasks by combining human intelligence and machine intelligence. Specifically, this article presents a paradigm of a human–machine traded control systems based on reinforcement learning methods to optimize the solution process of sequential decision problems. By designing the idea of autonomous boundary and credibility assessment, we enable humans and machines at the decision-making level of the systems to collaborate more effectively. And the arbitration in the humanmachine traded control systems introduces the Bayesian neural network and the dropout mechanism to consider the uncertainty and security constraints. Finally, experiments involving machine traded control, human traded control were implemented. The preliminary experimental results of this article show that our traded control method improves decision-making performance and verifies the effectiveness for SDM problems.

8. DoS攻击下基于自适应事件触发的无人水面艇航向控制和故障检测 赵云波, 王岭人, and 叶泽华 高技术通讯 2022 [Abs] [pdf]

   针对网络能力受限和非周期DoS攻干扰的网络化USV系统，提出一种基于自适应事件触发的故障检测滤波器和控制器的设计方法。首先，构建一个考虑非周期DoS攻击、外部干扰和执行器故障同时存在的USV控制系统。然后，针对网络化USV系统，提出一种自适应事件触发机制，动态更新触发阈值，减少网络资源浪费。其次，通过构造一个分段Lyapunov函数，给出闭环系统全局指数稳定且具有指定H_∞干扰衰减指数的充分条件，并设计基于观测的故障检测滤波器和控制器。最后，通过仿真验证方法的有效性。结果表明，该方法不仅能对USV系统航向进行有效控制，而且能在节省网络资源的同时检测执行器故障的发生和位置。

Conference Articles

1. Shared Autonomy Based on Human-in-the-loop Reinforcement Learning with Policy Constraints Ming Li, Yu Kang, Yun-Bo Zhao , Jin Zhu, and Shiyi You In 2022 41st Chinese Control Conference (CCC) 2022 [Abs] [doi] [pdf]

   In shared autonomous systems, humans and agents cooperate to complete tasks. Since reinforcement learning enables agents to train good policies through trial and error without knowing the dynamic model of the environment, it has been well applied in shared autonomous systems. After inferring the target from human inputs, agents trained by RL can accurately act accordingly. However, existing methods of this kind have big problems: the training of reinforcement learning algorithms require lots of exploration, which is time-consuming, lack of security guarantee and likely to cause great losses in the training process. Moreover, most of shared control methods are human-oriented, and do not consider the situation that humans may make wrong actions. In view of the above problems, this paper proposes human-in-the-loop reinforcement learning with policy constraints. In the training process, human prior knowledge is used to constrain the exploration of agents to achieve fast and efficient learning. In the process of testing we incorporate policy constraints in the arbitration to avoid serious consequences caused by human mistakes.

2. A Robustness Benchmark for Prognostics and Health Management Binkun Liu, Yun-Bo Zhao , Yang Cao, Yu Kang, and Zhenyi Xu In 2022 41st Chinese Control Conference (CCC) 2022 [Abs] [doi] [pdf]

   With the rise of intelligent manufacturing, prognostics and health management(PHM) has developed rapidly as an important part of intelligent manufacturing.Existing deep learning-based PHM methods are data-dependent. However, sensor data often contains noise and is redundant and high-dimensional, making it difficult for the PHM methods to learn a stable set of model parameters, so the methods are likely to be wrong when disturbed. However, the factory hopes that the PHM methods are robust enough to adapt to various disturbances, so it is necessary to perform robustness evaluation on the existing methods in advance for easy deployment. Although the existing robust theoretical analysis methods for neural networks can obtain tight robust boundaries, they consume a lot of computing resources and are difficult to scale to large neural networks. To slove this problem, We design a benchmark for robustness analysis of large deep learning PHM models, in which we test the model robustness using a variety of perturbations to simulate the actual production environment of the factory. Specifically, Gaussian noise is used to test the robustness of the model to background noise; random mask is used to test the robustness of the model to data loss. We hope that our robustness benchmark can serve as a reference for designing PHM models to improve the robustness of factory PHM models.

3. Strategy Generation Based on DDPG with Prioritized Experience Replay for UCAV Junsen Lu, Yun-Bo Zhao , Yu Kang, Yuhui Wang, and Yimin Deng In 2022 International Conference on Advanced Robotics and Mechatronics (ICARM) 2022 [Abs] [doi] [pdf]

   Unmanned combat aerial vehicles are becoming essential participants in future air-combat scenarios, while the optimal control strategy remains a great challenge due to the high dynamics of the aerial vehicles themselves as well as the environmental uncertainties in air-combat. Based on a deep deterministic policy gradient algorithm framework, an air combat decision-making strategy is designed and implemented, and further a prioritized experience replay method is proposed for the proposed algorithm to further improve the efficiency in the training process. Simulation experiments show that, at much reduced training cost, the proposed approach achieves superior air combat performance with fast convergence.

4. UAV Swarm Confrontation Based on Multi-agent Deep Reinforcement Learning Zhi Wang, Fan Liu, Jing Guo, Chen Hong, Ming Chen, Ershen Wang, and Yun-Bo Zhao In 2022 41st Chinese Control Conference (CCC) 2022 [Abs] [doi] [pdf]

   Multi-agent deep reinforcement learning (MADRL) has attracted a tremendous amount of interest in recent years. In this paper, we introduce MADRL into the confrontation scene of Unmanned Aerial Vehicle (UAV) swarm. To analysis the dynamic game process of UAV swarm confrontation, we build two non-cooperative game models based on MADRL paradigm. By using the multi-agent deep deterministic policy gradient (MADDPG) and the centralized training with decentralized execution method, we achieve the Nash equilibrium under 5 vs. 5 UAV confrontation scenes. We also introduce multi-agent soft actor critic (MASAC) method into the UAV swarm confrontation, simulation results indicate that MASAC-based model outperforms MADDPG-based model on exploring the UAV swarm combat environment, and more effectively converges to the Nash equilibrium. Our work will provide new insights into the confrontation of UAV swarm.

5. Air Combat Maneuver Decision Based on Deep Reinforcement Learning and Game Theory Shuhui Yin, Yu Kang, Yun-Bo Zhao , and Jian Xue In 2022 41st Chinese Control Conference (CCC) 2022 [Abs] [doi] [pdf]

   The autonomous maneuver decision of UA V plays an important role in future air combat. However, the strong competitiveness of the air combat environment and the uncertainty of the opponent make it difficult to solve the optimal strategy. For these problems, we propose the algorithm based on deep reinforcement learning and game theory, which settles the matter that the existing methods cannot solve Nash equilibrium strategy in highly competitive environment. Specifically, 1 vl air combat is modeled as a two-player zero-sum Markov game, and a simplified two-dimensional simulation environment is constructed. We prove that the algorithm has good convergence through the simulation test. Compared with the opponent’s strategy using DQN, our algorithm has better air combat performance and is more suitable for the air combat game environment.

6. Equipment Health Assessment Based on AHP-CRITIC Dynamic Weight Yunsheng Zhao, Pengfei Li, Tao Wang, Yu Kang, and Yun-Bo Zhao In 2022 41st Chinese Control Conference (CCC) 2022 [Abs] [doi] [pdf]

   Prognostics Health and Management (PHM) has become a hot research problem with the improvement of different equipment. Besides, it is significant to assess the health status of equipment in PHM because an accurate health assessment can guide maintenance plans for engineers. To accurately reflect equipment health status by an index, an assessment method based on AHP-CRITIC dynamic weight is proposed in this paper. Analytic Hierarchy Process (AHP) is a subjective method used to evaluate the importance of different indicators. The criteria importance through inter-criteria correlation (CRITIC) method is used to calculate the contrast intensity of the same indicator and the conflict between indicators and obtain the objective weights. A set of more scientific weights is gained by combining the weights obtained from AHP and CRITIC, respectively. Moreover, to reflect each indicator’s real impact on overall health status, a dynamic weight adjustment mechanism is used. The case study of suction nozzles of a specific type of chip mounter shows that this method can reflect the health status accurately.

7. Anomaly Detection for Surface of Laptop Computer Based on Patchcore Gan Algorithm Huijuan Zhu, Yu Kang, Yun-Bo Zhao , Xiaohui Yan, and Junqiang Zhang In 2022 41st Chinese Control Conference (CCC) 2022 [Abs] [doi] [pdf]

   Timely detection of notebook appearance defects is an important means to prevent products from being delivered to customers before leaving the factory.In industrial production, more emphasis is placed on fast and accurate detection methods, but the existing difficulties: 1. Defect samples are rare and difficult to obtain; 2. In high-resolution images, there are slight differences between abnormal samples and normal samples; 3. Slowly detection and insufficient accuracy.The existing methods mainly use a large amount of abnormal samples, so it is difficult to extend to the field of notebook appearance anomaly detection.To solve this problem, we designed a method that firstly uses unsupervised PatchCore which the algorithm was trained on normal samples and Defect GAN is used in test phase. To create a large number of verisimilitude abnormal samples and test these samples with PatchCore. On TKP-Surface datasets, the AUROC score of image-level anomaly detection achieves 96.1%, which meets the requirements of industrial applications.

Book Chapters

1. SMT Component Defection Reassessment Based on Siamese Network Chengkai Yu, Yun-Bo Zhao , and Zhenyi Xu In Methods and Applications for Modeling and Simulation of Complex Systems 2022 [Abs] [doi] [pdf]

   In the SMT process, after component placement, checking the quality of component placement on the PCB board is a basic requirement for quality control of the motherboard. In this paper, we propose a deep learning-based classification method to identify the quality of component placement. This is a comparison method and the novelty is that the siamese network is trained to extract the features of the standard placement component map and the placement component map to be inspected and output the probability of similarity between the two to determine the goodness of the image to be inspected. Compared to traditional hand-crafted features, features extracted using convolutional neural networks are more abstract and robust. In addition, during training, the concatenated network pairs the sample images to expand the amount of training data, increasing the robustness of the network and reducing the risk of overfitting. The experimental results show that this method has better results than the general model for the classification of placement component images.

2021

Journal Articles

1. 利用人的分歧介入增强珍珠自动分拣可靠性研究 花婷婷, 王岭人, and 赵云波 计算机测量与控制 2021 [Abs] [pdf]

   面向珍珠自动分拣应用场景，研究提出了一种通过人的分歧介入提升分拣可靠性的方法。该方法引入两个独立 AI 系统用于珍珠分拣的预处理，然后通过二者之间的分歧引入人的介入干预，在较少的人力成本下达到了对机器算法可 靠性的提升。定义了包括分歧准确指数和额外成本指数在内的性能评价指标，在公开的珍珠数据集上，研究提出的方法以 4.1%的额外人工成本提升了近 4%的珍珠分拣精度，验证了方法的有效性。

2. Estimation Based Approximating Control for Wireless Networked Control Systems Qipeng Liang, Qiaohui Zhu, Yu Kang, and Yun-Bo Zhao J. Univ. Sci. Tech. China 2021 [Abs] [doi] [pdf]

   The control design and system analysis of wireless networked control systems with unknown roundtrip delay characteristics are investigated. An estimation based approximating control strategy is proposed to stabilize the systems by using delay characteristics in a practically feasible way. The strategy first uses a delay transition probability estimator to obtain the delay characteristics estimation by measuring delay data online, and then uses an approximating controller to take advantage of the estimation. On this basis, a packet delay variation detector is designed, making the strategy adaptive to the variation of delay characteristics. The sufficient conditions to ensure the closed-loop system being mean-square uniformly ultimately bounded are given, with also the controller gain design method. The effectiveness of the proposed approach is verified numerically.

3. Networked Dual-Mode Adaptive Horizon MPC for Constrained Nonlinear Systems Pengfei Li, Yu Kang, Yun-Bo Zhao , and Tao Wang IEEE Trans. Syst. Man Cybern, Syst. 2021 [Abs] [doi] [pdf]

   This article investigates the predictive control scheme and related stability issue for a class of discrete-time perturbed nonlinear system with state and input constraints. First, we propose a novel control framework, i.e., networked dual-mode adaptive horizon model predictive control (MPC), which consists of a local controller, a remote controller that is subject to packet losses, and a judger coordinating the switchings between them. The optimization procedure of MPC with variable prediction horizon is implemented in the remote controller while a simple state-feedback control law is in the local one. Second, to establish the stability condition, we propose a new Lyapunov function. By specifying the relation between the Lyapunov function and the optimal MPC value function, the input-to-state practical stability is established. Finally, simulation results show the effectiveness of our proposed control scheme.

4. A Novel Self-Triggered MPC Scheme for Constrained Input-Affine Nonlinear Systems Pengfei Li, Yu Kang, Yun-Bo Zhao , and Tao Wang IEEE Trans. Circuits Syst. II 2021 [Abs] [doi] [pdf]

   This brief develops a novel self-triggered model predictive control algorithm based on time delay estimation for perturbed input-affine nonlinear systems. At each triggering instant, the algorithm determines simultaneously the predictive control sequence to feedforward compensate for the disturbance and the next triggering instant. As a consequence, the unnecessary samplings and transmissions are suppressed, and the frequency of solving the model predictive controller is reduced. The feasibility of the scheme as well as the associated stability are verified, with a numerical example illustrating the effectiveness of the proposed scheme.

5. A Novel Inertial-Visual Heading Determination System for Wheeled Mobile Robots Wenjun Lv, Yu Kang, Yun-Bo Zhao , Yuping Wu, and Wei Xing Zheng IEEE Trans. Contr. Syst. Technol. 2021 [Abs] [doi] [pdf]

   Finding an alternative way to replace the magnetic compass to determine the robot heading angle indoor is always a challenge in the robotics society. This paper proposes a structurally simple yet efficient non-magnetic heading determination system, which can be used in the planar indoor environment with abundant ferro- and electro-magnetic interferences, by the combination of gyroscope and vision. The gyroscope is utilized to perceive the yaw rate, while a downward-looking camera is used to capture the pre-laid auxiliary strips to acquire the absolute angle of the robot heading. Due to the existence of pseudo measurement, varying noise statistical characteristics, and asynchronization between state propagation and measurement, the existing Kalman filters cannot be applied to fuse the gyroscopic and visual data. Therefore, a novel fusion algorithm named pseudo-measurement-resistant adaptive asynchronous Kalman filter is proposed, which is experimentally verified to be efficient in the environment with various interferences.

6. A Characteristic Modeling Method of Error-Free Compression for Nonlinear Systems Bin Meng, Yun-Bo Zhao , and Jing-Jing Mu Control Theory Technol. 2021 [Abs] [doi] [pdf]

   The existence of error when compressing nonlinear functions into the coefficients of the characteristic model is known to be a key issues in existing characteristic modeling approaches, which is solved in this work by proposing an error-free compression method. We first define a key concept of the relevant states with corresponding compressing methods into their coefficients, where the coefficients are continuous and bounded and the compression is error-free. Then, we give the conditions for decoupling characteristic modeling for MIMO systems, and sequentially, we establish characteristic models for nonlinear systems with minimum phase and relative order two as well as the flexible spacecrafts, realizing the equivalence and solving the cyclic demonstration problem in the characteristic model theory. Finally we also explicitly explain the reasons for normalization in the characteristic model theory.

7. The Dynamics Characteristics of Flexible Spacecraft and Its Closed-Loop Stability with Passive Control Bin Meng, and Yun-Bo Zhao J Syst Sci Complex 2021 [Abs] [doi] [pdf]

   Passive control is the most popular methodology for flexible spacecraft while it remains an open problem whether the closed-loop performance can be achieved only with passive control subject to the coupling modes of rigid and flexibility. Also, the closed-loop performance of passive PD control based on the dynamics of the Euler angle parameterization of spacecraft, which has been widely used in practice, is yet to be addressed. Towards these challenges, by introducing the input-output exact linearization theory and Lyapunov theory, we show that the closed-loop performance for flexible spacecraft with rigid and flexible modes can be achieved by adjusting the parameters of the passive controllers sufficiently large. This is done by firstly transforming the flexible spacecraft dynamics into an exact feedback linearization standard form, and then analyzing the closed-loop performance of flexible spacecraft.

8. Robust Approximation-Based Event-Triggered MPC for Constrained Sampled-Data Systems Tao Wang, Yu Kang, Pengfei Li, Yun-Bo Zhao , and Peilong Yu J Syst Sci Complex 2021 [Abs] [doi] [pdf]

   In this paper, an approximation-based event-triggered model predictive control (AETMPC) strategy is proposed to implement event-triggered model predictive control for continuous-time constrained nonlinear systems under the digital platform. In our AETMPC strategy, both of the optimal control problem (OCP) and the triggering conditions are defined in discrete-time manner based on approximate discrete-time models, while the plant under control is continuous time. In doing so, sensing load is alleviated because the triggering condition does not need to be checked continuously, and the computation of the OCP is simpler since which is calculated in the discrete-time framework. Meanwhile, robust constraints are satisfied in continuous-time sense by taking inter-sampling behaviour into consideration, and a novel constraint tightening approach is presented accordingly. Furthermore, the feasibility the AETMPC strategy is analyzed and the associated stability of the overall system is established. Finally, this strategy is validated by a numerical example.

9. Construction and Analysis of Cortical–Muscular Functional Network Based on Eeg-Emg Coherence Using Wavelet Coherence Xugang Xi, Ziyang Sun, Xian Hua, Changmin Yuan, Yun-Bo Zhao , Seyed M. Miran, Zhizeng Luo, and Zhong Lü Neurocomputing 2021 [Abs] [doi] [pdf]

   Research on the brain functional network is important in understanding the normal function of the brain and diagnosing neuropsychiatric diseases. Inspired by the brain functional network, we constructed a cortical–muscular functional network using electroencephalography and electromyography to explore the motion control mechanism of the central nervous system and understand the organization and coordination mechanisms of limb motion control. In the process of constructing the network, 12 signal acquisition channels were selected as nodes, and the wavelet coherence is used as the index of connection between network nodes. Based on the original network, we used a fixed weighted edge and threshold methods to remove weak weighted edges and compare the performance of the two methods. The experimental results showed that the constructed network had a higher clustering coefficient, and the smaller characteristic path length indicated a small-world characteristic. At the same time, the weighted characteristic path length and weighted clustering coefficient of the functional network simplified by the threshold method can show promising classification accuracy under Fisher and artificial neural network.

10. Cortico-Muscular Functional Network: An Exploration of Cortico-Muscular Coupling in Hand Movements Xugang Xi, Xiangxiang Wu, Yun-Bo Zhao , Junhong Wang, Wanzeng Kong, and Zhizeng Luo J. Neural Eng. 2021 [Abs] [doi] [pdf]

    Objective. The main objective of this research was to study cortico-muscular, intra-cortical, and inter-muscular coupling. Herein, we established a cortico-muscular functional network to assess the network differences associated with making a fist, opening the hand, and wrist flexion. Approach. We used transfer entropy to calculate the causality between electroencephalographic and electromyographic data and established the transfer entropy connection matrix. We then applied graph theory to analyze the clustering coefficient, global efficiency, and small-world attributes of the cortico-muscular functional network. We also used Relief-F to extract the features of the transfer entropy connection matrix of the beta2 band for the different hand movements and observed high accuracy when this feature was used for action recognition. Main result. We found that the cortico-muscular functional network of the three actions in the beta band had small-world attributes, among which the beta2 band’s small-world was stronger. Moreover, we found that the extracted features were mainly concentrated in the left frontal area, left motor area, occipital lobe, and related muscles, suggesting that the cortico-muscular functional network could be used to assess the coupling differences between the cortex and the muscles that are associated with different hand movements. Overall, our results showed that the beta2 (21–35 Hz) wave is the main information carrier between the cortex and the muscles, and the cortico-muscular functional network can be used in the beta2 band to assess cortico-muscular coupling. Significance. Our study preliminarily explored the cortico-muscular functional network associated with hand movements, providing additional insights regarding the transmission of information between the cortex and the muscles, thereby laying a foundation for future rehabilitation therapy targeting pathological cortical areas in stroke patients.

11. Effect of Muscle Fatigue on the Cortical-Muscle Network: A Combined Electroencephalogram and Electromyogram Study Xugang Xi, Shaojun Pi, Yun-Bo Zhao , Huijiao Wang, and Zhizeng Luo Brain Research 2021 [Abs] [doi] [pdf]

    Electroencephalogram (EEG) and electromyogram (EMG) signals during motion control reflect the interaction between the cortex and muscle. Therefore, dynamic information regarding the cortical-muscle system is of significance for the evaluation of muscle fatigue. We treated the cortex and muscle as a whole system and then applied graph theory and symbolic transfer entropy to establish an effective cortical-muscle network in the beta band (12–30 Hz) and the gamma band (30–45 Hz). Ten healthy volunteers were recruited to participate in the isometric contraction at the level of 30% maximal voluntary contraction. Pre- and post-fatigue EEG and EMG data were recorded. According to the Borg scale, only data with an index greater than 14<19 were selected as fatigue data. The results show that after muscle fatigue: (1) the decrease in the force-generating capacity leads to an increase in STE of the cortical-muscle system; (2) increases of dynamic forces in fatigue leads to a shift from the beta band to gamma band in the activity of the cortical-muscle network; (3) the areas of the frontal and parietal lobes involved in muscle activation within the ipsilateral hemibrain have a compensatory role. Classification based on support vector machine algorithm showed that the accuracy is improved compared to the brain network. These results illustrate the regulation mechanism of the cortical-muscle system during the development of muscle fatigue, and reveal the great potential of the cortical-muscle network in analyzing motor tasks.

12. Emotion-Movement Relationship: A Study Using Functional Brain Network and Cortico-Muscular Coupling Xugang Xi, Qun Tao, Jingqi Li, Wanzeng Kong, Yun-Bo Zhao , Huijiao Wang, and Junhong Wang Journal of Neuroscience Methods 2021 [Abs] [doi] [pdf]

    Background: Emotions play a crucial role in human communication and affect all aspects of human life. However, to date, there have been few studies conducted on how movements under different emotions influence human brain activity and cortico-muscular coupling (CMC). New methods: In this study, for the first time, electroencephalogram (EEG) and electromyogram physiological electrical signals were used to explore this relationship. We performed frequency domain and nonlinear dy­ namics analyses on EEG signals and used transfer entropy to explore the CMC associated with the emotionmovement relationship. To study the transmission of information between different brain regions, we also constructed a functional brain network and calculated various network metrics using graph theory. Results: We found that, compared with a neutral emotional state, movements made during happy and sad emotions had increased CMC strength and EEG power and complexity. The functional brain network metrics of these three emotional states were also different. Comparison with existing methods: Much of the emotion-movement relationship research has been based on subjective expression and external performance. Our research method, however, focused on the processing of physiological electrical signals, which contain a wealth of information and can objectively reveal the inner mechanisms of the emotion-movement relationship. Conclusions: Different emotional states can have a significant influence on human movement. This study presents a detailed introduction to brain activity and CMC.

13. Simultaneous and Continuous Estimation of Joint Angles Based on Surface Electromyography State-Space Model Xugang Xi, Wenjun Jiang, Xian Hua, Huijiao Wang, Chen Yang, Yun-Bo Zhao , Seyed M. Miran, and Zhizeng Luo IEEE Sensors J. 2021 [Abs] [doi] [pdf]

    Simultaneous and continuous joint angle estimation plays an important role in motion intention recognition and rehabilitation training. A surface electromyography (sEMG) state-space model is proposed to estimate simultaneous and continuous lower-limb-joint movements from sEMG signals in this paper. The model combines the forward dynamics with Hill-based muscle model (HMM), making the extended model capable of estimating the lower-limb-joint motion directly. sEMG features including root-mean-square and wavelet coefficients are then extracted to construct a measurement equation used to reduce system error and external disturbances. With the proposed model, unscented Kalman filter is used to estimate joint angle from sEMG signals. In the experiments, sEMG signals were recorded from ten subjects during muscle contraction involving three lower-limb-joint motions (knee-joint motion, ankle-joint motion, and simultaneous knee-ankle-joint motion). Comprehensive experiments are conducted on three motions and the results show that the mean root–mean–square error for knee-joint motion, ankle-joint motion, and simultaneous motion of the proposed model are 5.1143°, 5.2647°, and 6.3941°, respectively, and significant improvements are demonstrated compared with the traditional methods.

14. Effects of Transcranial Direct Current Stimulation on Brain Network Connectivity and Complexity in Motor Imagery Kangbo Yang, Xugang Xi, Ting Wang, Junhong Wang, Wanzeng Kong, Yun-Bo Zhao , and Qizhong Zhang Neuroscience Letters 2021 [Abs] [doi] [pdf]

    Related experiments have shown that transcranial direct current stimulation (tDCS) anodal stimulation of the brain’s primary motor cortex (M1) and supplementary motor area (SMA) can improve the motor control and clinical manifestations of stroke patients with aphasia and dyskinesia. In this study, to explore the different effects of tDCS on the M1 and SMA in motor imagery, 35 healthy volunteers participated in a double-blind randomized controlled experiment. Five subjects underwent sham stimulation (control), 15 subjects under­ went tDCS anode stimulation of the M1, and the remaining 15 subjects underwent tDCS anode stimulation of the SMA. The electroencephalogram data of the subjects’ left- and right-hand motor imagery under different stim­ ulation paradigms were recorded. We used a functional brain network and sample entropy to examine the different complexities and functional connectivities in subjects undergoing sham-tDCS and the two stimulation paradigms. The results show that tDCS anodal stimulation of the SMA produces less obvious differences in the motor preparation phase, while tDCS anodal stimulation of the M1 produces significant differences during the motor imaging task execution phase. The effect of tDCS on the motor area of the brain is significant, especially in the M1.

15. Model-Based Network Scheduling and Control for Systems Over the Ieee 802.15.4 Network Yun-Bo Zhao , Deheng Xu, Jiangtao He, Xu-Gang Xi, and Yu Kang J Syst Sci Complex 2021 [Abs] [doi] [pdf]

    The scheduling and control of a class of wireless networked control system is investigated, whose control loop is closed via a shared IEEE 802.15.4 wireless network. By using a gain scheduler within the packet-based control framework and fitting the delay-dependent gains into a time-delay system model, a less conservative self-triggered approach is proposed to determine the sampling update, which consequently enables the design of two network scheduling algorithms to reduce the communication usage. Numerical and TrueTime based examples illustrate the effectiveness of the proposed approach in the sense that it reduces greatly the communication usage while maintaining satisfactory control performance.

16. Predictive Event-Triggered Control for Disturbanced Wireless Networked Control Systems Yun-Bo Zhao , Xiaokang Pan, and Shiming Yu J Syst Sci Complex 2021 [Abs] [doi] [pdf]

    The control and scheduling for wireless networked control system with packet dropout and disturbance are investigated. A prediction based event triggered control is proposed to reduce data transmissions while preserving the robustness against external disturbance. First, a trigger threshold is especially designed to maintain the difference of the estimated and actual states below a proper boundary when system suffers from packet dropout. Then a predictive controller is designed to compensate for packet dropouts by utilizing the packet-based control approach. The sufficient conditions to ensure the closed-loop system being uniformly ultimately bounded are derived, with consequently the controller gain method. Numerical examples illustrate the effectiveness of the proposed approach.

17. 浅谈控制中的共享信息和共享自主 赵云波, 康宇, and 朱进 系统与控制纵横 2021 [Abs] [pdf]

    控制系统是由受控对象、传感器、控制器、 执行器等不同组件有机构成的随时间演化的一 个整体，控制理论研究控制系统的动态行为，和前馈、反馈等控制机制对系统动态行为的改造， 而控制工程则依托控制理论解决各种实际应用 领域中的控制问题。与绝大多数信息和工程技术 不同的是，控制是一种使能技术，并不局限于某 一特定应用领域，而是在现代科技的几乎所有方 方面面都起着某种关键基础性作用。 从宏大的技术发展的角度来看，控制的发展 与人类历史的技术变革相伴相生，而技术变革也 对控制提供了不同的机遇和相伴而来的要求和 挑战。首先，起于 19 世纪末的工业革命掀起了 轰轰烈烈的以机器替代人的劳动的革命，在其中 控制赋能的自动化起到了关键基础性的作用；其 次，伴随着始于上个世纪末、至今仍方兴未艾的 信息革命的发展，远程、大规模、网络化的控制 系统和自动化应用极大的拓展了控制的版图； 最后，近些年才重新兴盛起来但潜力无限的人工 智能（Artificial Intelligence, AI）技术，则带来了 变革性的机器自主性革命，将控制系统的研究带 入了一个崭新的领域。 本文试图简单讨论信息革命和 AI 自主革命 带给控制的若干机遇和挑战，特别的，分别关注 其中的“共享信息”和“共享自主”控制范式。 本文并不打算对这两个相关领域给出详尽的理 论和方法上的介绍，而是较为科普性质的，目的 是通过对问题的来源和其重要性的讨论，希望能 够吸引更多研究者投入到这两个相关联领域的 研究当中来。

18. Multi-Path Switching Protection for Networked Control Systems Under Unbounded DoS Attacks Qiaohui Zhu, Qipeng Liang, Yu Kang, and Yun-Bo Zhao Journal of University of Science and Technology of China 2021 [Abs] [doi] [pdf]

    The strategy design and closed-loop stability of networked control systems under unbounded denial of service (DoS) attacks are investigated. A multi-path switching protection strategy is firstly designed by noticing the usually available multiple paths in data communication networks. The strategy consists of a DoS attack detection module at the actuator side to identify DoS attacks from normal data packet dropouts, and a multi-path switching module at the sensor side to effectively switch the data transmission path when necessary. Then, the sufficient conditions for the closed-loop system being global mean square asymptotic stability are given, with a corresponding controller gain design method. Numerical examples illustrate the effectiveness of the proposed approach.

Conference Articles

1. Self-Triggered Model Predictive Control for Perturbed Nonlinear Systems: An Iterative Implementation Tao Wang, Pengfei Li, Yu Kang, and Yun-Bo Zhao In 2021 60th IEEE Conference on Decision and Control (CDC) 2021 [Abs] [doi] [pdf]

   In this paper, a novel iterative self-triggered model predictive control strategy is proposed for continuous-time nonlinear systems with external disturbance. For this strategy, the triggering instants are determined by iteratively using the self-triggered mechanism. To be specific, the triggering mechanism, on the one hand, determines the next sampling instants of the sensor by a prespecified condition, and, on the other hand, decides whether or not to treat the current sampling instant as the triggering instant. Without continuous monitoring of the state, the sensing cost of the sensor can be alleviated. The utilization of the sampling states after the triggering instant leads to a larger triggering interval, and the computational load of the controller can thus be reduced. The effectiveness of the proposed strategy is validated by a numerical example.

2. Approximation-Based Self-Triggered Model Predictive Control for Perturbed Nonlinear Systems Chang Xu, Yu Kang, Yun-Bo Zhao , Pengfei Li, and Tao Wang In 2021 China Automation Congress (CAC) 2021 [Abs] [doi] [pdf]

   This paper proposes an approximation-based selftriggered model predictive control strategy for perturbed constrained nonlinear sampled-data systems. In our proposed strategy, the finite horizon optimal control problem (FHOCP) and the triggering condition are designed based on approximate discrete-time models. By implementing the strategy, the computation problem of the FHOCP becomes tractable since it is computed in a discrete-time framework. Meanwhile, the next triggering instant is pre-determined by the triggering condition, reducing the sensing cost and the computing frequency of the FHOCP. Furthermore, feasibility of the FHOCP and stability of the overall system are analyzed. Finally, a simulation example verifies the effectiveness of the strategy.

3. Adaptive Arbitration for Minimal Intervention Shared Control via Deep Reinforcement Learning Shiyi You, Yu Kang, Yun-Bo Zhao , and Qianqian Zhang In 2021 China Automation Congress (CAC) 2021 [Abs] [doi] [pdf]

   In shared control, humans and intelligent robots jointly complete real-time control tasks with their complementary capabilities for improved performance unavailable by neither side on its own, which is attracting more and more attentions in recent years. Arbitration, as an indispensable part of shared control, determines how control authority is allocated between the human and robot, and the definition of that policy has always been one of the fundamental problems. In this paper, we propose an adaptive arbitration method for shared control systems, which minimizes the deviation from the human inputs while ensuring the system performance based on deep reinforcement learning. We provide humans the maximum assistance with the minimal intervention, in order to balance human’s need for control authority and need for performance. We apply our method to real-time control tasks, and the results show that our method achieves high task success rate and shorter task completion time with less human workload, while maintaining higher human satisfaction.

Books

1. 人机混合智能系统自主性理论和方法 赵云波, 康宇, and 朱进 科学出版社 2021 [Abs] [pdf]

   人工智能（Artificial Intelligence, AI）技术的迅猛发展可能是科技领域近些年最为令人激动人心的现象之一。很少有一项具体的技术引发社会如此广泛的关注，除了技术专家，哲学、法律、道德、社会、管理、经济等各领域的专家学者都在其中各执一词，普通民众也同样表现出极大的热情，莫衷一是。这充分表现出AI技术对我们整个世界方方面面的巨大影响力，也预示着这一技术重塑我们整个世界的巨大潜力。 而谈及未来，一个与我们每个人都息息相关的话题就摆上桌面。在AI从业者那里，这一话题事关所谓的“强弱人工智能之辩”，其中的关键疑问是：现有的仅在某些方面强于人类的“专用人工智能”是否在未来会进化到在所有方面都强于人的“通用人工智能”？而在广大的民众那里，这一强弱人工智能之辩则关乎个人福祉甚至人类命运和尊严：高度发展的专用人工智能可以让创造性含量少的工作丢掉前景，而通用人工智能则更进一步会让整个人类失掉存在价值。这一话题早已在报刊、网络、电视各种公共舆论空间引发广泛的讨论，在真正的未来到来之前，激烈的讨论也远不会有消失的可能。 我们在此无需对这一充满科幻感的话题抛出我们的观点：从目前的技术发展现状来说，这一话题的讨论更多的出于信念，而非依据坚实的技术细节可以做出的可信技术展望。我们只强调如下的事实：AI技术的发展使得由其赋能的机器越来越具有更强大的智能自主能力，并且在越来越多的领域得到了应用，在可预见的未来AI技术的应用似乎还没有减速的迹象。 这一事实把一个原本并不存在或至少并不重要的问题推到了我们面前：在未来的世界里，人的智能和AI赋能的机器智能将无处不在的共存共生，如何在二者之间进行有效融合将成为科学研究的一个重要主题。 人的智能和机器智能在未来的融合共生正是本书的关注点，但本书的主题将更为具体的局限于自动化控制相关的技术领域中。我们认为，人的智能和机器智能在自动化控制领域的共融共存导致了所谓的“人机混合智能系统”的出现，这一新型的系统形式和智能形式在两方面具有本质的重要性：一方面，从自动化控制角度来说，人机混合智能系统所代表的系统结构形式是传统自动化控制系统应对AI赋能的机器智能变革的必然发展形式；另一方面，从智能科学的角度来说，人机混合智能系统所代表的智能形式也成为人工智能未来发展的重要甚至是唯一的终极形式。这两方面本质上的重要性使得建立相关领域的理论和方法框架变得极为急迫和重要。 在本书中，我们试图抛砖引玉，对这一全新而重要的研究领域提供虽然初步但仍然是系统的思考。我们并不希望过多执着于个人的自尊，浅薄地认为本书所提出的理论和方法是面向这一全新领域的必由之路；我们最大的愿望，不过是借由此书，谦卑地展示这一前景广阔而意义重大的研究领域，吸引更多的年轻学者投身其中。

2020

Conference Articles

1. Event-Triggered Adaptive Horizon Model Predictive Control for Perturbed Nonlinear Systems Pengfei Li, Tao Wang, Yu Kang, and Yun-Bo Zhao In 2020 59th IEEE Conference on Decision and Control (CDC) 2020 [Abs] [doi] [pdf]

   This paper proposes a new event-triggered adaptive horizon model predictive control (MPC) for discrete-time nonlinear systems with additive disturbance. With the eventtriggered control scheme, the MPC is solved only at triggering instant and the event is triggered if the difference between the actual state and the predicted state exceeds the triggering threshold. The triggering threshold depends on the prediction horizon and becomes larger as the state approaches the terminal constraint set. Therefore, larger triggering intervals can then be obtained. Finally, a numerical example shows the effectiveness of the proposed scheme.

2. Synthesis of Wireless Networked Control System Based on Round-trip Delay Online Estimation Liang Lu, Qipeng Liang, Qiaohui Zhu, and Yun-Bo Zhao In 2020 Chinese Automation Congress (CAC) 2020 [Abs] [doi] [pdf]

   A control design approach with the integration of online delay estimation is proposed for wireless networked control systems (WNCSs) with unknown round-trip delay, which improves control performance in a practically feasible way. We introduce a delay probability estimation unit to obtain the delay characteristics by estimating the delay when the control system is running. We also present a piecewise approximation control strategy to take advantage of the estimation. Furthermore, the control gain is synthesized with stability guarantee. The conditions to ensure the stochastic stability of the closed-loop system are given, and the effectiveness of the proposed approach is verified numerically.

3. Detection of Distracted Driving Based on Multi-Granularity and Middle-Level Features Min Tang, Fang Wu, Li-Li Zhao, Qi-Peng Liang, Jian-Wu Lin, and Yun-Bo Zhao In 2020 Chinese Automation Congress (CAC) 2020 [Abs] [doi] [pdf]

   A so-called MGMN (Multiple-GranularityMiddle Network) algorithm is proposed to improve the detection accuracy of distracted driving, based on multigranularity features and middle-level features. The algorithm is derived from the ResNet50 neural network and is the first time to use multi-granularity features and mid-level features of images in the field of distracted driving detection. The multigranularity feature extraction module consists of three branches: the global branch to learn the global features without local information, the second branch to divide the image level into two parts and later to learn the local features of the upper and lower parts, and the third branch to divide the image level into three parts, and later to learn the local features of the upper, middle and lower parts. By extracting the features of the middle layer of the image, the feature extraction of the algorithm is enriched. While the multi-granularity features are individually input to the cross-entropy loss function, the multi-granularity features of the image and the middle-level features are combined and input into the cross-entropy loss function. The proposed algorithm has an accuracy of 99.8% on the dataset "Districted-DriverDetection" published by State Farm Company, which is 1.5% to 3% higher than existing algorithms, and an improved accuracy of 98.7% on the dataset "AUC-Districted-Driver-Detection".

4. Autonomous Boundary of Human-Machine Collaboration System Based on Reinforcement Learning Qianqian Zhang, Yun-Bo Zhao , and Yu Kang In 2020 Australian and New Zealand Control Conference (ANZCC) 2020 [Abs] [doi] [pdf]

   This paper provides a human-machine collaborative control framework, including artificial intelligence decision systems, human-level control, arbiter judgment, and learning of autonomous boundary, so that human suggestions are incorporated into the training process of decisions, assisting agents to learn quickly control decision tasks. Based on the model-free deep reinforcement learning algorithm HITL-AC, the human feedback (reward or punishment) is connected with the reward of the agent, so that the agent continuously tries to find a better boundary during the system’s operation, avoiding defects of pre-fixed boundary. This formulation improves the data efficiency of reinforcement learning and plays a guiding role in seeking human intervention when the agent is in an uncertain environmental state during the test use phase. The fourth section of the paper gives a training demonstration of the bipedal walker. The experimental results show that human intervention can accelerate the process of agent reinforcement learning during the training phase, and seek human help when guiding the dangerous state of the agent during the test phase. This is beneficial for solving real-world problems, further proving the feasibility and effectiveness of the proposed framework and method.

Journal Articles

1. 基于改进困难三元组损失的跨模态行人重识别框架 李灏, 唐敏, 林建武, and 赵云波 计算机科学 2020 [Abs] [doi] [pdf]

   为了提升跨模态行人重识别算法识别精度，提出了一种基于改进困难三元组损失特征学习框架。首先，改进了 传统困难三元组损失，使其转换为全局三元组损失。其次，基于跨模态行人重识别中存在模态间变化及模态内变化问 题，设计模态间三元组损失及模态内三元组损失配合全局三元组损失进行模型训练。在改进困难三元组损失基础上， 首次在跨模态行人重识别模型中设计属性特征来增加模型提取特征能力。最后，针对跨模态行人重识别中出现类别失 衡问题，首次引用Focal Loss 替代传统交叉熵损失进行模型训练。相比现有算法，在RegDB 数据集实验中，本文框 架在各项指标中均有1.9%-6.4%的提升。另外，通过消融实验也证明了三种方法均能提升模型特征提取能力。

2. Robust Model Predictive Control for Constrained Networked Nonlinear Systems: An Approximation-Based Approach Tao Wang, Yu Kang, Pengfei Li, Yun-Bo Zhao , and Peilong Yu Neurocomputing 2020 [Abs] [doi] [pdf]

   In this paper, a robust approximation-based model predictive control (RAMPC) scheme for the constrained networked control systems (NCSs) subject to external disturbances is proposed. At each sampling instant, the approximate discrete-time model (DTM) is utilized for solving the optimal control problem online, and the control input applied to continuous-time systems can then be determined. Such RAMPC scheme enables to implement MPC for the continuous-time systems in the digital environment, and meanwhile, achieves the state and control input constraints satisfaction in continuous-time sense. Furthermore, we also provide a guideline to determine the allowable sampling period. Sufficient conditions for the feasibility of the RAMPC scheme as well as the associated stability are developed. Finally, the effectiveness of the RAMPC scheme is shown through a numerical simulation.

3. 基于无偏线性最优估计的PET图像重建 王宏霞, 徐英婕, 赵云波, and 张文安 计算机科学 2020 [Abs] [doi] [pdf]

   正电子发射断层成像( PositronEmissionTomo g ra p h y , PET ) 技术在实体肿瘤的定性诊断和病灶转移的检查中具有举 足轻重的作用,因此非常有必要提高 PET 的成像质量.然而,已有的迭代重建算法基本上都严重依赖于 PET 的线性模型.考 虑到探测器效率、探测系统的几何尺寸、生物组织对光子的衰减以及散射效应等诸多物理因素,该模型无法真实地刻画示踪剂 与正弦图数据之间的复杂关系.文中首先提出了一种新的观测模型,通过在原来的线性模型中引入未知输入项来刻画示踪剂 与正弦图数据之间的关系.该项由两部分组成: １ ) 系数矩阵,用于进一步描述投影的线性部分; ) 未知输入,用于刻画示踪剂的 ２ 浓度分布和投影数据之间的一些非线性关系.在此新模型的基础上, PET 图像重构问题被转化成一个线性无偏的最优估计问 题.然后,给出了具有待定增益的线性迭代估计模型,通过将正弦数据向未知输入项的系数矩阵的零空间零域上进行投影,消 除了未知输入给线性最优估计带来的困难,借助卡尔曼滤波的设计思路,推导出了前述的估计增益.基于此估计模型,提出了 一种基于无偏线性最优估计的重建算法.最后,通过仿真实验,将所提重建算法与期望极大估计算法( Ex p ectationＧMaximizaＧ 以及基于标准卡尔曼滤波( 的重 tionreconstruction , EM )、 核化的 EM 算法( Kernelmethod , KEM ) KalmanFilterin gm ethod , KF ) 建算法从均方误差( MeanS q uareError , MSE )、 信噪比( Si g nalＧNoiseＧRate , SNR ) 两个方面进行了比较.实验结果表明:与其他 ３ 种算法相比,所提算法重建的图像具有更大的信噪比、更小的均方误差,视觉上更加清晰,更好地重建了肿瘤的形状和尺寸, 因此具有更好的重构质量.

4. Enhanced EEG–EMG Coherence Analysis Based on Hand Movements Xugang Xi, Cunbin Ma, Changmin Yuan, Seyed M. Miran, Xian Hua, Yun-Bo Zhao , and Zhizeng Luo Biomedical Signal Processing and Control 2020 [Abs] [doi] [pdf]

   Electroencephalogram (EEG)–electromyogram (EMG) coherence analysis is an effective method for examining the functional connection between brain and muscles. An improved coherence approach is proposed in this study to enhance the estimation of EEG–EMG coherence. First, we sampled the synchronous EEG signal based on the burst points of the EMG signal. Then, a moving average of the sampled EEG by using a window function is performed before the EEG is sampled again on the basis of the EMG burst points. The EEG signals are reassembled to effectively reflect the muscle motions. Finally, the estimation of the EEG–EMG coherence is computed by using magnitude square coherence (MSC) and wavelet coherence. The coherence characteristics of the different autonomous movements in the -band and band are analyzed to verify the reliability of the method. Results show that our proposed method can remarkably enhance EEG–EMG coherence estimation regardless of using either MSC or wavelet coherence. The results of coherence analysis not only can correctly reflect the coupling relationship between the cortex and the muscles but can also distinguish the EEG–EMG coherences of the different autonomous movements.

5. Facial Expression Distribution Prediction Based on Surface Electromyography Xugang Xi, Yan Zhang, Xian Hua, Seyed M. Miran, Yun-Bo Zhao , and Zhizeng Luo Expert Systems with Applications 2020 [Abs] [doi] [pdf]

   Facial expression recognition plays an important role in research on human-computer interaction. The common facial expressions are mixtures of six basic emotions: anger, disgust, fear, happiness, sadness, and surprise. The current study, however, focused on a single basic emotion on the basis of physiological signals. We proposed emotion distribution learning (EDL) based on surface electromyography (sEMG) for predicting the intensities of basic emotions. We recorded the sEMG signals from the depressor supercilii, zygomaticus major, frontalis medial, and depressor anguli oris muscles. Six features were extracted in the frequency, time, time-frequency, and entropy domains. Principal component analysis (PCA) was used to select the most representative features for prediction. The key idea of EDL is to learn a function that maps the PCA-selected features to the facial expression distributions such that the special description degrees of all basic emotions for an emotion can be learned by EDL. Simultaneously, Jeffrey’s divergence considered the relationship between different basic emotions. The performance of EDL was compared with that of multilabel learning based on PCA-selected features. Predicted results were measured by six indices, which could reflect the distance or similarity degree between distributions. We conducted an experiment on six different emotion distributions. Experimental results show that the EDL can predict the facial expression distribution more accurately than the other methods.

6. Feature Extraction of Surface Electromyography Based on Improved Small-World Leaky Echo State Network Xu-Gang Xi, Wenjun Jiang, Seyed M Miran, Xian Hua, Yun-Bo Zhao , Chen Yang, and Zhizeng Luo Neural Computation 2020 [Abs] [doi] [pdf]

   Surface electromyography (sEMG) is an electrophysiological reflection of skeletal muscle contractile activity that can directly reflect neuromuscular activity. It has been a matter of research to investigate feature extraction methods of sEMG signals. In this letter, we propose a feature extraction method of sEMG signals based on the improved small-world leaky echo state network (ISWLESN). The reservoir of leaky echo state network (LESN) is connected by a random network. First, we improved the reservoir of the echo state network (ESN) by these networks and used edge-added probability to improve these networks. That idea enhances the adaptability of the reservoir, the generalization ability, and the stability of ESN. Then we obtained the output weight of the network through training and used it as features. We recorded the sEMG signals during different activities: falling, walking, sitting, squatting, going upstairs, and going downstairs. Afterward, we extracted corresponding features by ISWLESN and used principal component analysis for dimension reduction. At the end, scatter plot, the class separability index, and the Davies-Bouldin index were used to assess the performance of features. The results showed that the ISWLESN clustering performance was better than those of LESN and ESN. By support vector machine, it was also revealed that the performance of ISWLESN for classifying the activities was better than those of ESN and LESN.

7. sEMG-MMG State-Space Model for the Continuous Estimation of Multijoint Angle Xu-Gang Xi, Chen Yang, Seyed M Miran, Yun-Bo Zhao , Shuliang Lin, and Zhizeng Luo Complexity 2020 [Abs] [doi] [pdf]

   Continuous joint angle estimation plays an important role in motion intention recognition and rehabilitation training. In this study, a surface electromyography- (sEMG-) mechanomyography (MMG) state-space model is proposed to estimate continuous multijoint movements from sEMG and MMG signals accurately. The model combines forward dynamics with a Hill-based muscle model that estimates joint torque only in a nonfeedback form, making the extended model capable of predicting the multijoint motion directly. The sEMG and MMG features, including the Wilson amplitude and permutation entropy, are then extracted to construct a measurement equation to reduce system error and external disturbances. Using the proposed model, a closed-loop prediction-correction approach, unscented particle filtering, is used to estimate the joint angle from sEMG and MMG signals. Comprehensive experiments are conducted on the human elbow and shoulder joint, and remarkable improvements are demonstrated compared with conventional methods.

8. 随机有界通信时延下传感器网络中的一致性时钟同步算法 余世明, 周景远, 何德峰, and 赵云波 控制与决策 2020 [Abs] [doi] [pdf]

   在存在随机有界时延的情况下， 现有的许多一致性时钟同步算法的同步过程是发散的。 本文在平均一 致性时钟同步算法（Average TimeSynch, ATS）的基础上， 通过对偏斜和偏移同步过程进行分析， 找出了导致 同步过程发散的原因。通过改变相对偏移估计的方法，保证了偏斜同步过程的收敛。在偏移同步过程中，通过 计算节点的同步误差，得出了时延条件下偏移同步误差有界的结论。最后通过仿真表明，本文提出的时钟同步 算法在存在随机有界时延且保证网络连通的条件下，同步过程收敛且同步误差有界。

9. 大批量订单整体分拣问题建模及其分布式并行方法 赵云波, 李天舒, and 汪钰皓 高技术通讯 2020 [Abs] [pdf]

   对大批量订单中的货物进行高效整体分拣是影响大型电商整体物流效率的一个关键因素，但这一问题尚缺少严格模型和有效解决方法。本文通过对订单分拣系统的静态状态和动态演化进行数学刻画，并对订单分拣算法、系统限制条件和分拣效率等进行严格定义和区分，建立了大批量订单整体分拣问题的数学模型。在此基础上，利用智能货架思想提出了一种大批量订单的分布式并行整体分拣方法，有效解决了在订单整体分拣问题中的打包点数量瓶颈问题，解决了所提出方法的关键技术难点，并在数值仿真下证明了该方法的有效性。

10. 无线网络化控制系统的功率感知事件触发策略及其闭环稳定性 赵云波, 袁征, and 朱创 控制理论与应用 2020 [Abs] [doi] [pdf]

    研究了能量受限无线网络化控制系统的设计和分析问题. 首先建立了无线网络化控制系统中数据误码率 和丢包率的定量关系, 进而设计了基于事件触发策略和功率优化机制的智能控制器. 在随机稳定框架下给出了相 应闭环系统稳定的充分性条件. 数值例子证明了所提出方法的有效性.

Book Chapters

1. 人机混合的智能控制 赵云波 In 智能控制:方法与应用 2020 [pdf]

2019

Journal Articles

1. Destabilization of Eukaryote mRNAs by 5’ Proximal Stop Codons Can Occur Independently of the Nonsense-Mediated mRNA Decay Pathway Barbara Gorgoni, Yun-Bo Zhao , Jawahar Krishnan, and Ian Stansfield Cells 2019 [Abs] [doi] [pdf]

   In eukaryotes, the binding of poly(A) binding protein (PAB) to the poly(A) tail is central to maintaining mRNA stability. PABP interacts with the translation termination apparatus, and with eIF4G to maintain 3′–5′ mRNA interactions as part of an mRNA closed loop. It is however unclear how ribosome recycling on a closed loop mRNA is influenced by the proximity of the stop codon to the poly(A) tail, and how post-termination ribosome recycling affects mRNA stability. We show that in a yeast disabled for nonsense mediated mRNA decay (NMD), a PGK1 mRNA with an early stop codon at codon 22 of the reading frame is still highly unstable, and that this instability cannot be significantly countered even when 50% stop codon readthrough is triggered. In an NMD-deficient mutant yeast, stable reporter alleles with more 3′ proximal stop codons could not be rendered unstable through Rli1-depletion, inferring defective Rli1 ribosome recycling is insufficient in itself to trigger mRNA instability. Mathematical modelling of a translation system including the effect of ribosome recycling and poly(A) tail shortening supports the hypothesis that impaired ribosome recycling from 5′ proximal stop codons may compromise initiation processes and thus destabilize the mRNA. A model is proposed wherein ribosomes undergo a maturation process during early elongation steps, and acquire competency to re-initiate on the same mRNA as translation elongation progresses beyond the very 5′ proximal regions of the mRNA.

2. FVC: A Novel Non-Magnetic Compass Wenjun Lv, Yu Kang, and Yun-Bo Zhao IEEE Transactions on Industrial Electronics 2019 [Abs] [doi] [pdf]

   The accurate orientation measurement in re- al time contributes significantly to the control of mobile robots, and further assists them to realize some funda- mental functions like automatic pilot, cargo delivery, target tracking, etc. The traditional magnetic compass has been denounced for its susceptibility to ferrous or electric ma- terials, vehicular motion, and latitude variation. Hence, in this paper, we aim at proposing a novel non-magnetic com- pass named floor visual compass (FVC) for mobile robots working in indoor scenarios, which is mainly implemented by a downward-looking monocular camera. With previously laid auxiliary strips on the floor, which are parallel to the reference axis, the FVC is able to estimation the robot’s orientation by means of image processing technologies and interval arithmetics. Considering the computational complexity of the visual orientation measurement, an event trigger for FVC is designed, to reduce the frequency of the correction operation using the visual orientation measure- ment. The real-world experiment verifies the effectiveness of the proposed non-magnetic compass.

3. 计算受限控制系统的一种全资源预测控制方法 马翠芹, 赵云波, 姚俊毅, and 韩康 自动化学报 2019 [Abs] [doi] [pdf]

   针对具有时变有限且不可预知计算资源的控制系统，提出了一种充分利用可用计算资源的预测控制策略和相应的控制器设计方法。该策略在控制系统可用计算资源充足时计算多步前向预测控制量，进而使用合适预测控制量在控制器因缺少计算资源无法运行时闭合 系统，达到了在不要求额外计算资源前提下提升控制系统性能的效果，利用改进的模型预测控制方法设计了相应的控制器，并分别使用纯数值和Matlab/LabVIEW联合仿真算例对所提出的方法进行了验证。

4. Denoising of Surface Electromyogram Based on Complementary Ensemble Empirical Mode Decomposition and Improved Interval Thresholding Xu-Gang Xi, Yan Zhang, Yun-Bo Zhao , Qingshan She, and Zhizeng Luo Review of Scientific Instruments 2019 [Abs] [doi] [pdf]

   Surface electromyogram (sEMG) signals are physiological signals that are widely applied in certain fields. However, sEMG signals are frequently corrupted by noise, which can lead to catastrophic consequences. A novel scheme based on complementary ensemble empirical mode decomposition (CEEMD), improved interval thresholding (IT), and component correlation analysis is developed in this study to reduce noise contamination. To solve the problem of losing desired information from sEMG, an sEMG signal is first decomposed using CEEMD to obtain intrinsic mode functions (IMFs). Subsequently, IMFs are selected via component correlation analysis, which is a measure used to select relevant modes. Thus, each selected IMF is modified through improved IT. Finally, the sEMG signal is reconstructed using the processed and residual IMFs. Root-mean-square error (RMSE) and signal-to-noise ratio (SNR) are introduced as evaluation criteria for the sEMG signal from the standard database. With SNR varying from 1 dB to 25 dB, the...

5. Surface Electromyography-Based Daily Activity Recognition Using Wavelet Coherence Coefficient and Support Vector Machine Xu-Gang Xi, Chen Yang, Jiahao Shi, Zhizeng Luo, and Yun-Bo Zhao Neural Processing Letters 2019 [Abs] [doi] [pdf]

   Daily activity monitoring plays an important role among frail or elderly people and has caught attention. Surface electromyography (sEMG) can extract the feature of activity, but it is not stable because of electrode displacement, postural changes, and individual- dependent features, such as the condition of muscles, subcutaneous fat, and skin surface. To effectively extract the feature of sEMG signal, we proposed a new method of feature extraction based on coherence analysis. The sEMG signals were recorded from gastrocne- mius, tibialis anterior, rectus femoris, and semitendinosus. After de-noising, sEMG signals were decomposed into 32-scale by wavelet transformation, and their wavelet coefficients were employed to calculate wavelet coherence coefficients (WCC). We employed T test to find out if the coherence between sEMG signals was statistically different among six activi- ties. The 32nd scale WCC of RF–ST and ST–TA as eigenvector was entered into the sup- port vector machine (SVM). The six activities, namely, standing, walking, running, stair- ascending, stair-descending, and falling, were successfully identified by the WCC feature with the SVM classifier.

6. 基于神经网络模型的计量泵远程流量广义预测控制 余世明, 张航, 何德峰, and 赵云波 高技术通讯 2019 [Abs] [pdf]

   为了在流程工业中实现流体物料的远程投加，提出了一种隔膜计量泵流量远程控制 方案。方案采用上层优化节点、双信道通信网络和本地控制器的分级控制策略，利用神经网 络辨识计量泵流量和电机转速的动态模型，并设计了基于自适应广义预测控制(GPC)的流量 调节算法。通过对比仿真和实际平台验证了所提出方案的可行性和有效性。

7. A Brief Tutorial and Survey on Markovian Jump Systems: Stability and Control Ping Zhao, Yu Kang, and Yun-Bo Zhao IEEE Syst. Man Cybern. Mag. 2019 [Abs] [doi] [pdf]

   A brief tutorial and survey for the stability analysis and control approaches for Markovian jump systems is provided. Various stability notions for Markovian jump systems are first defined, and several commonly used control approaches to such systems are then presented. These discussions consist of both the fundamental concepts and definitions, and the-state-of-the-art of the recent developments in the literature. Such a combination gives a general picture of the field.

8. Classification-Based Control for Wireless Networked Control Systems with Lossy Multipacket Transmission Yun-Bo Zhao , Jiang-Tao He, Qiao-Hui Zhu, Tao Huang, and Shi Ming Yu IEEJ Transactions on Electrical and Electronic Engineering 2019 [Abs] [doi] [pdf]

   A classification-based control approach is proposed for wireless networked control systems with lossy multi-packet transmission. This approach takes advantage of a state reconstruction process to deal with the distinct feature of partial failure of data transmission caused by multi-packet transmission, and then classifies the difference of the latest received system states and and reconstructed ones to design a classification-based controller. The closed-loop stability of the system is proven using the switched systems theory. By considering more communication characteristics of multi-packet transmission, the proposed approach is shown to give rise to a better system performance by a numerical example.

9. HPILN: A Feature Learning Framework for Cross-Modality Person Re-Identification Yun-Bo Zhao , Jian-Wu Lin, and Xu-Gang Xi IET Image Processing 2019 [Abs] [doi] [pdf]

   Most video surveillance systems use both RGB and infrared cameras, making it a vital technique to re-identify a person cross the RGB and infrared modalities. This task can be challenging due to both the cross-modality variations caused by heterogeneous images in RGB and infrared, and the intra-modality variations caused by the heterogeneous human poses, camera position, light brightness etc. To meet these challenges, a novel feature learning framework, hard pentaplet and identity loss network (HPILN), is proposed. In the framework existing single-modality re-identification models are modified to fit for the cross-modality scenario, following which specifically designed hard pentaplet loss and identity loss are used to increase the accuracy of the modified cross-modality re-identification models. Based on the benchmark of the SYSU-MM01 dataset, extensive experiments have been conducted, showing that the authors’ method outperforms all existing ones in terms of cumulative match characteristic curve and mean average precision.

10. 多径路由网络化控制系统的路径调度与控制器协同设计 赵云波, 姚俊毅, and 倪洪杰 系统科学与数学 2019 [Abs] [pdf]

    研究了带有多径通信路由的网络化控制系统的通信路径调度和控制器的协同设计问题。通过将不同通信路径切换及带来的时延变化建模为系统模态的切换，得到了所研究系统的切换系统模型。给出了使得闭环系统指数稳定的通信路径调度所需满足的条件，并提出了满足系统稳定和网络负载均衡的闭环通信路径调度方案和控制器设计方法。数值仿真算例验证了算法的优越性和有效性。

Conference Articles

1. Channel-Aware Scheduling for Multiple Control Systems with Packet-Based Control over Collision Channels Pengfei Li, Yu Kang, Yun-Bo Zhao , and Xiaokang Pan In 2019 American Control Conference (ACC) 2019 [Abs] [doi] [pdf]

   We consider a wireless control architecture with multiple control systems communicating over two shared col- lision channels. Each sensor accesses the channel randomly and a scheduler at the controller side decides which controller is permitted to access the channel. We design a packet-based model predictive controller and obtain the packet transmission success probability demands of stability. The channel-aware transmission strategy of each sensor is studied in the non- cooperative game theory framework. We also characterize the Nash equilibrium and design a decentralized channel access mechanism to achieve the Nash equilibrium. The effectiveness of our results is demonstrated by a numerical simulation.

2018

Conference Articles

1. State Estimation with Multi-Packet Transmission Over the Wireless Network Jiang-Tao He, and Yun-Bo Zhao In Chinese Control Conference 2018 [Abs] [doi] [pdf]

   In wireless control, the measurement of a system may be divided into multiple partial observations for transmission purpose, known as ”multi-packet transmission”. The state estimation for the discrete linear time-invariant system with the multi- packet transmission is considered. We focus on what the filter has received during each time step. For the randomness of the received packets, the classical Kalman filter is revised to the time-varying Kalman filter. With the concept of combination and the supposed delivery rate for each packet, we analyze the stability of the filter in the sense of expectation. To deal with the combination explosion problem, alternative conditions on delivery rates is provided.

2. Packet-Based Model Predictive Control for Networked Control Systems With Random Packet Losses Pengfei Li, Yu Kang, Yun-Bo Zhao , and Zheng Yuan In IEEE Conference on Decision and Control 2018 [Abs] [doi] [pdf]

   In this paper, the stability for a class of nonlinear networked control systems with a model predictive controller (MPC) is investigated. Both the sensor-to-controller channel and the controller-to-actuator channel suffer from random packet losses. By constructing a novel cost function, and studying its deviation from the original MPC cost function, we establish the stochastic stability for the closed-loop system. To guarantee the stability, the relationship between the prediction horizon and the packet loss probabilities of two channels is also discussed. Finally, the effectiveness of our results is demonstrated by a numerical example.

3. Terrain Vision Aided Online Estimation of Instantaneous Centers of Rotation for Skid-Steering Mobile Robots Wenjun Lv, Ji Chang, Yu Kang, Yun-Bo Zhao , and Zerui Li In IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER) 2018 [Abs] [doi] [pdf]

   Skid-steering mobile robots suffer from slip effect inevitably during their turnings, which results in imprecise kinematics model and the degradation of navigation and control performances. Hence, in this paper, we aim at developing an online estimation method to acquire the robot’s instantaneous centers of rotation (ICRs), a kind of slip parameters, by means of data fusion technologies. The sensor system is composed of two incremental encoders, a compass, a Global Positioning System (GPS) unit, a camera and a data fusion unit. Based on the data gathered from these sensors, the data fusion unit is able to provide accurate global location, absolute heading and robot’s ICRs in real time by applying the proposed terrain adaptive innovation-based extended Kalman filter. With the aid of terrain vision, the process noise covariance can be adjusted according to the terrain type adaptively, and therefore, the ICR estimation converges rapidly and smoothly. The real-world experiment conducted on a four-wheel mobile robot is exhibited to validate the effectiveness. Additionally, the results show that the terrain adaptive odometry has higher accuracy than the traditional ones.

4. 多径路由网络化控制系统的建模和稳定性分析 赵云波, and 姚俊毅 In 中国控制会议 2018 [Abs] [pdf]

   研究了基于多径路由的网络化控制系统，对多径路由中存在的时延和丢包进行建模，并对由于长时延和多路径 丢包所导致的控制信息的乱序进行分析，并将其建立成有限状态的Markov链的形式。利用基于包的控制方法针对不同 的执行器端时延的情况设计控制器。最后数值仿真验证了算法的优越性和有效性。

Journal Articles

1. A Novel Location Strategy for Minimizing Monitors in Vehicle Emission Remote Sensing System Yu Kang, Zerui Li, Yun-Bo Zhao , Jiahu Qin, and Weiguo Song IEEE Transactions on Systems, Man, and Cybernetics: Systems 2018 [Abs] [doi] [pdf]

   The vehicle emission remote sensing system is one promising solution to monitor the emissions of on-road vehicles that contribute to the air pollution in urban areas. To implement such a system an effective location strategy to place the moni- tors is yet to be designed. To this purpose we formulate a novel location problem where the minimum subset of roads on which traffic emission monitors are located is to be found only using the topological structure and some other available information of the traffic network. We solve this problem by transforming it into a graph-theoretic problem and considering more character- istics such as the traffic regulations and limits. After modeling the real-world traffic network as a digraph, a two-step algo- rithm is developed. The first step is to find all directed circuits to establish hypergraph-based set of directed circuits using the depth first searching strategy. In the second step, an approxima- tion algorithm is designed to find the greedy transversal which is a subset of roads to place vehicle emission monitors in order to cover all the traffic circuits. The performance of the loca- tion strategy is validated by both theoretical developments and illustrative examples.

2. Discretization-Based Stabilization for a Class of Switched Linear Systems with Communication Delays Pengfei Li, Yu Kang, Yun-Bo Zhao , Jiahu Qin, and Weiguo Song ISA Transactions 2018 [Abs] [doi] [pdf]

   The stabilization problem for a class of switched linear systems is investigated in the network environment. Both the synchronous and asynchronous cases are considered according to the availability of the current activated system mode to the actuator. The random communication delay is assumed to be Markovian, resulting in a sampled-data synchronous or asynchronous switched system with Markovian delay as the closed-loop system. We extend the discretization approach to deal with such sampled-data system through exploring the stability conditions of the corresponding discrete-time system. For the asynchronous case, we formulate the closed-loop system as a hybrid system with the switching between its subsystems governed by a switching signal and a Markov chain. By studying the switching number and one-step reachable mode set of the constructed vector- valued switching signal, the exponential mean-square stability (EMSS) conditions and the corresponding mode- dependent controller are obtained with a more general constraint on the designed switching signal. These results are finally verified by two illustrated numerical examples.

3. Self-Tuning Asynchronous Filter for Linear Gaussian System and Applications Wenjun Lv, Yu Kang, and Yun-Bo Zhao IEEE/CAA Journal of Automatica Sinica 2018 [Abs] [doi] [pdf]

   In this paper, the filtering problem for linear Gaussian system is considered. We will propose a self-tuning asyn- chronous filter subject to measurement-lossy situation where the measurements are available at equal sampling intervals, namely, the process and measurement equations are asynchronous at every time but synchronous every several times. What is more, considering the variation of noise statistics, a regular called noise variance estimator is introduced to adjust filter’s coefficients adaptively. The case studies of wheeled robot navigation system and air quality evaluation system will show the practicability in engineering and some characteristics.

4. Bipartite Consensus of Discrete-Time Double-Integrator Multi-Agent Systems with Measurement Noise Cui-Qin Ma, Weiwei Zhao, and Yun-Bo Zhao Journal of Systems Science and Complexity 2018 [Abs] [doi] [pdf]

   The effects of measurement noise are investigated in the context of bipartite consensus of multi-agent systems. In the system setting, discrete-time double-integrator dynamics are assumed for the agent, and measurement noise is present for the agent receiving the state information from its neighbors. Time-varying stochastic bipartite consensus protocols are designed in order to lessen the harmful effects of the noise. Consequently, the state transition matrix of the closed-loop system is analyzed, and sufficient and necessary conditions for the proposed protocol to be a mean square bipartite consensus protocol are given with the help of linear transformation and algebraic graph theory. It is proven that the signed digraph to be structurally balanced and having a spanning tree are the weakest communication assumptions for ensuring bipartite consensus. In particular, the proposed protocol is a mean square bipartite average consensus one if the signed digraph is also weight balanced.

5. Bipartite Linear χ-Consensus of Double-Integrator Multi-Agent Systems With Measurement Noise Cui-Qin Ma, Weiwei Zhao, and Yun-Bo Zhao Asian Journal of Control 2018 [Abs] [doi] [pdf]

   The bipartite consensus problem is investigated for double-integrator multi-agent systems in the presence of measurement noise. A distributed protocol with time-varying consensus gain is proposed. By using...

6. Event-Triggered Bipartite Consensus of Single-Integrator Multi-Agent Systems with Measurement Noise Cui-Qin Ma, Yun-Bo Zhao , and Wei-Guo Sun Journal of Control Science and Engineering 2018 [Abs] [doi] [pdf]

   Event-triggered bipartite consensus of single-integrator multi-agent systems is investigated in the presence of measurement noise. A time-varying gain function is proposed in the event-triggered bipartite consensus protocol to reduce the negative effects of the noise corrupted information processed by the agents. Using the state transition matrix, Itô formula, and the algebraic graph theory, necessary and sufficient conditions are given for the proposed protocol to yield mean square bipartite consensus. We find that the weakest communication requirement to ensure the mean square bipartite consensus under event-triggered protocol is that the signed digraph is structurally balanced and contains a spanning tree. Numerical examples validated the theoretical findings where the system shows no Zeno behavior.

7. Prediction-Based Approach to Output Consensus of Heterogeneous Multi-Agent Systems with Delays Chong Tan, Xiao Yin, Guo-Ping Liu, Jinjie Huang, and Yun-Bo Zhao IET Control Theory & Applications 2018 [Abs] [doi] [pdf]

   The output consensus of multi-agent systems is investigated, where constant communication delay is present and the dynamics of the agents are heterogeneous. Based on the networked predictive control scheme, the distributed consensus protocol with dynamic output feedback controller is designed, and the sufficient conditions of the output consensus are obtained. Numerical examples illustrate the effectiveness of the proposed method.

8. 考虑地球自转的探月返回跳跃式再入制导研究 王泽国, 孟斌, and 赵云波 控制理论与应用 2018 [Abs] [doi] [pdf]

   本文考虑探月返回飞行器跳跃式再入的制导问题. 针对考虑地球自转的探月返回飞行器不确定非线性动 力学, 设计了基于观测器的近似反馈线性化制导律. 由于初始再入时空气密度近似为零, 因此阻力初始观测误差近 似为零. 针对该特点, 研究了基于观测器的控制律的峰值问题, 给出了无峰值现象的控制律的简化设计方法. 首次 证明了速度内动态是有界输入有界状态的, 为制导律的设计提供了基础. 证明了制导误差是一致最终有界的, 并且 界是随着初始观测误差的减小而趋于零; 进一步由于阻力初始观测误差近似为零, 因此制导误差近似为零. 最后, 针对Apollo命令舱, 通过数学仿真验证了本文方法的有效性. 本文为包括火星再入、月球再入等飞行器的制导问题 提供了研究途径和基础.

9. Delayed Feedback MPC Algorithms of Vehicle Platoons Subject to Constraints on Measurement Range and Driving Behaviors Shi Ming Yu, Sai Nan Wu, Yun-Bo Zhao , and De Feng He Asian Journal of Control 2018 [Abs] [doi] [pdf]

   The control problem of vehicle platoons considering sensors with limited measurement range and actuator time delay is investigated in the face of constraints. A new delayed feedback model predictive control scheme is proposed to solve the prob- lem while satisfying the constraints on measurement range and driving behaviors. A family of controllers is presented with free parameters which are then computed by online solving of a receding horizon optimal control problem. Terminal equality con- straints are adopted to guarantee stability of the closed-loop system. Some sufficient conditions with guaranteed string stability of the platoon and zero steady-state error are established. The effectiveness and advantages of the presented method are dem- onstrated by simulating two classical road scenarios.

10. Stochastic Stabilisation of Wireless Networked Control Systems with Lossy Multi-Packet Transmission Yun-Bo Zhao , Tao Huang, Yu Kang, and Xu-Gang Xi IET Control Theory & Applications 2018 [Abs] [doi] [pdf]

    The stochastic stabilisation of networked control systems is investigated with a special focus on the lossy multi-packet transmission in the wireless communication context. The resulting partially available system states due to multi-packet transmission are firstly reconstructed at the controller, and the sufficient conditions for stochastic stability are then given for the closed-loop system, which finally leads to a controller design method with explicit consideration of multi-packet transmission. The proposed theoretical results are verified by both numerical and TrueTime-based examples.

11. Using Communication Networks in Control Systems: The Theoretical and Practical Challenges Yun-Bo Zhao , Zhihong Man, Jongrae Kim, and Cui-Qin Ma Journal of Control Science and Engineering 2018 [doi] [pdf]

Books

1. Packet-Based Control for Networked Control Systems Yun-Bo Zhao , Guo-Ping Liu, Yu Kang, and Li Yu Springer Nature Singapore Pte Ltd. 2018 [Abs] [doi] [pdf]

   Networked control systems (NCSs) are control systems whose control links are closed via some form of communication networks. It has become a useful control system model in recent years due to the fast development of the embedded com- putational devices and the communication technology. These developments have made it possible that a large number of sensors, actuators and controllers can be interconnected over the communication network to interact with the physical environment. This remote and distributed control system structure is the basis of a great many of future applications in information technology, including Internet of Things, cyber-physical systems, smart home. NCSs can contain a large number of control devices interconnected, and data is exchanged through communication networks which inevitably introduces com- munication constraints to the control system, e.g. network-induced delay, data packet dropout, data packet disorder, data rate constraint. These communication constraints in NCSs present great challenges for conventional control theory. The study of NCSs therefore requires multi-field knowledge, and consequently the integration of control, communication and computations, i.e. the “co-design” approach. In this book, we report a class of co-design approach to NCSs—the “packet-based control” approach—which is achieved by taking advantage of the packet-based transmission of the communication network in NCSs, one primary feature distinct from conventional control systems. For completeness, an introductory chapter is first included which provides a brief tutorial of NCSs, and then the remainder of the book is organized into three parts, covering the design, analysis and extension of the packet-based control approach, respectively. These studies have shown that the packet-based control approach is both unified and flexible: on the one hand, the approach can stand on its own as a novel class of design and analysis methods different from existing ones; on the other, control methods can also be fitted into the packet-based control approach for a better system performance. A unique co-design framework, i.e. packet-based net- worked control systems, is thus finally constructed. We hope the reader will find this book useful for their understanding of and research on networked control systems.isting

2017

Journal Articles

1. A Networked Remote Sensing System for On-Road Vehicle Emission Monitoring Yu Kang, Yan Ding, Zerui Li, Yang Cao, and Yun-Bo Zhao Sci China Inf. Sci 2017 [Abs] [doi] [pdf]

   Vehicle emissions are a major source of urban air pollution, and therefore a real-time monitoring system can be very useful in analyzing such emission and consequently assisting the policy making process. In this work we discuss the principle and structure of three different types of remote sensing detectors, and also the key techniques to establish a networked remote sensing monitoring system. We finally conclude this paper with data analysis from some preliminary experiments.

2. Bipartite Consensus of Integrator Multi-Agent Systems with Measurement Noise Cui-Qin Ma, Zheng-Yan Qin, and Yun-Bo Zhao IET Control Theory & Applications 2017 [Abs] [doi] [pdf]

   The bipartite consensus problem for integrator multi-agent systems over signed fixed digraphs is investigated in the presence of measurement noise. A time-varying consensus gain is introduced and then a stochastic type protocol is proposed, whose performance is analysed using the state transition matrix of the closed-loop system. Necessary and sufficient conditions for ensuring a mean square bipartite consensus protocol are obtained in the presence of noise. Furthermore, in the absence of noise it is shown that these conditions are also necessary and sufficient for ensuring the bipartite consensus except for the quadratic integrability of the consensus gain. It is found that the signed digraph being structurally balanced and having a spanning tree are the weakest assumptions on connectivity for achieving bipartite consensus regardless of the measurement noise. In particular, if the signed digraph is structurally unbalanced, then under some mild conditions, the states of the closed- loop system converge to zero in mean square, regardless of the initial states.

3. Leader–Follower \∞Consensus of Linear Multi-Agent Systems with Aperiodic Sampling and Switching Connected Topologies Dan Zhang, Zhenhua Xu, Qing-Guo Wang, and Yun-Bo Zhao ISA Transactions 2017 [Abs] [doi] [pdf]

   This paper is concerned with the distributed H\ınfty consensus of leader–follower multi-agent systems with aperiodic sampling interval and switching topologies. Under the assumption that the sampling period takes values from a given set, a new discrete-time model is proposed for the tracking error system. For the multi-agent systems with time-varying sampling period, switching topologies and external dis- turbance, the considered tracking problem is converted to a robust H\ınfty control problem. With help of the Lyapunov stability theory, a sufficient condition for the existence of mode-dependent controller is es- tablished and it guarantees the exponential stability of tracking error system and a prescribed H\ınfty dis- turbance attenuation level. The influence of sampling period on the overall control performance is also discussed. Two simulation examples are given to show the effectiveness of the proposed control algo- rithm.

Books

1. Stability Analysis of Markovian Jump Systems Yu Kang, Yun-Bo Zhao , and Ping Zhao Springer Nature Singapore Pte Ltd. 2017 [Abs] [pdf]

   This book focuses on the stability analysis of Markovian jump systems (MJSs) with various settings and discusses its applications in several different areas. It also presents general definitions of the necessary concepts and an overview of the recent developments in MJSs. Further, it addresses the general robust problem of Markovian jump linear systems (MJLSs), the asynchronous stability of a class of nonlinear systems, the robust adaptive control scheme for a class of nonlinear uncertain MJSs, the practical stability of MJSs and its applications as a modelling tool for networked control systems, Markovian-based control for wheeled mobile manipulators and the jump-linear-quadratic (JLQ) problem of a class of continuous-time MJLSs. It is a valuable resource for researchers and graduate students in the field of control theory and engineering.

Conference Articles

1. Dynamic Event-Triggered Control for Networked Switched Linear Systems Pengfei Li, Yu Kang, Yun-Bo Zhao , and Jian Zhou In Chinese Control Conference 2017 [Abs] [doi] [pdf]

   A class of discrete-time networked switched control systems are investigated with event-triggered control. The closed- loop system is formulated as switched linear systems with time-varying delays, based on which the exponential stability con- ditions are obtained under the time delay system framework. The co-design conditions of the control gain and the triggering parameters are proposed. Finally, a comparison between the static event-triggered mechanism and the dynamic one is illustrated by a numerical example.

2. Analytic Solution to Indefinite Linear Quadratic Regulator for Stochastic Systems Hongxia Wang, and Yun-Bo Zhao In Chinese Control Conference 2017 [Abs] [doi] [pdf]

   This paper aims to deal with the indefinite linear quadratic regulator(ILQR) for stochastic systems. It provides both the analytic solution to the ILQR and the sufficient and necessary condition under which the ILQR is solvable. Different from the existed literature, we obtain the results in a novel way. In order to obtain a tighter necessary condition, we investigate the solution structure of a two-point boundary value problem for a stochastic differential equations directly but involve no complicated probabilistic derivation. Consider that there are close relationships between the linear game problem and H\ınfty control and ILQR, the idea in the paper can also be extended to solve these problems.

3. A Novel Static PET Image Reconstruction Method Hongxia Wang, and Yun-Bo Zhao In Chinese Automation Congress 2017 [Abs] [doi] [pdf]

   In this paper, we present a novel image recon- struction algorithm for positron emission tomography(PET). Almost all of existing reconstruction approaches assume that the measurement model for PET is linear equation with Gaussian white noise or energy-bounded noise, which only approximates the emission and detection of PET very roughly. In fact, the real situation is much more complicated than the one mentioned above and there must be something that is not be involved in the aforementioned model. Hence, in this paper, we establish a more general and vivid measurement model via involving an unknown input, and propose a reconstruction method based on the optimal filtering for the stochastic system with unknown input. The approach reconstructs the PET image effectively and its performance is evaluated with the computer-synthesized cardiac-phantom.

4. Categorizing Attractor-Effective Canalyzing Functions in Boolean Networks Yun-Bo Zhao , Hui Dong, and Hongjie Ni In IFAC-PapersOnLine 2017 [Abs] [doi] [pdf]

   Canalyzing Boolean functions have shown their popularity in various biological networks and established themselves to be biologically meaningful at the system level, marking their importance in the analysis of stability and robustness of such complex systems. Based on a matrix representation of Boolean networks due to the recently developed tool called semi- tensor product, we categorize canalyzing functions in terms of their capabilities of affecting the number of attractors in the Boolean network, which is one key index for the stability and robustness of Boolean networks. We show that there exist only three categories of attractor- effective canalyzing functions for any network size larger than 1, while the number of all the interested canalyzing functions is proportional to the square of the network size. We also give the explicit expression of the mean number of attractors with any length for Boolean networks with a single canalyzing function. Compared with Boolean networks without any canalyzing functions, we are able to show quantitatively how canalyzing functions can affect the mean number and length of attractors in Boolean networks for the first time.

5. Scheduling and Control Co-Design for Control Systems under Computational Constraints Yun-Bo Zhao , Hui Dong, and Hongjie Ni In IFAC-PapersOnLine 2017 [Abs] [doi] [pdf]

   A prediction-based approach is proposed for control systems with limited and time- varying computational resources. The limited and time-varying computational resources can make the control system run in an open-loop fashion which may severely degrade the system performance or even destabilize the system. This issue is dealt with by producing more than one forward control predictions when abundant computational resources are available, and then using these forward control predictions to close the system when the computational resources are insufficient to calculate real-time control signal. This achievement is made without additional requirement for the computational resources and can be regarded as a useful completion of the scheduling algorithms. With a controller designed using a modified model predictive control method, the effectiveness of the proposed approach is successfully illustrated by a numerical example.

2016

Journal Articles

1. Characteristic Model Based Adaptive Controller Design and Analysis for a Class of SISO Systems Jian-Fei Huang, Yu Kang, Bin Meng, Yun-Bo Zhao , and Haibo Ji Sci China Inf. Sci 2016 [Abs] [doi] [pdf]

   The design of an adaptive controller and stability analysis of the corresponding closed loop system are discussed for a class of SISO systems based on the characteristic model method. The obtained characteristic model is a second-order slow time-varying linear system with a compress mapping function for the system modeling error. The pole placement method is used to design the controller, and sufficient conditions for the stability of the closed loop system are obtained based on the robust control theory of slow time-varying systems with perturbations. The effectiveness of the proposed method is illustrated by two numerical examples.

2. Dynamic Data Packing Towards the Optimization of QoC and QoS in Networked Control Systems Yu Kang, and Yun-Bo Zhao Science China Technological Sciences 2016 [Abs] [doi] [pdf]

   A class of networked control systems is investigated whose communication network is shared with other applications. The design objective for such a system setting is not only the optimization of the...

3. On Input-to-State Stability of Switched Stochastic Nonlinear Systems under Extended Asynchronous Switching Yu Kang, Di-Hua Zhai, Guo-Ping Liu, and Yun-Bo Zhao IEEE Transactions on Cybernetics 2016 [Abs] [doi] [pdf]

   An extended asynchronous switching model is inves- tigated for a class of switched stochastic nonlinear retarded systems in the presence of both detection delay and false alarm, where the extended asynchronous switching is described by two independent and exponentially distributed stochastic processes, and further simplified as Markovian. Based on the Razumikhin- type theorem incorporated with average dwell-time approach, the sufficient criteria for global asymptotic stability in probability and stochastic input-to-state stability are given, whose importance and effectiveness are finally verified by numerical examples.

4. Recent Advances on the Theory and Applications of Hybrid Systems Rui Wang, Guo-Ping Liu, Yun-Bo Zhao , and Weiguo Xia Mathematical Problems in Engineering 2016 [doi] [pdf]
5. 基于神经网络的伺服机械手 LuGre 摩擦补偿控制 王三秀, 赵云波, and 陈光 北京工业大学学报 2016 [Abs] [pdf]

   针对伺服机械手系统的 LuGre 摩擦模型参数辨识难,难以建立其精确的数学模型,利用径向基函数( RBF) 神经网络的万能逼近特性逼近 LuGre 摩擦,并作为计算转矩控制器的补偿项. 通过 Lyapunov 方法证明了系统的稳 定性以及闭环系统跟踪误差的收敛性. 仿真结果证明控制算法能对摩擦进行有效补偿,提高了伺服机械手系统的 轨迹跟踪控制性能.

6. Probabilistic Boolean Network Modelling and Analysis Framework for mRNA Translation Yun-Bo Zhao , and J. Krishnan IEEE/ACM Trans. Comput. Biol. and Bioinf. 2016 [Abs] [doi] [pdf]

   mRNA translation is a complex process involving the progression of ribosomes on the mRNA, resulting in the synthesis of proteins, and is subject to multiple layers of regulation. This process has been modelled using different formalisms, both stochastic and deterministic. Recently, we introduced a Probabilistic Boolean modelling framework for mRNA translation, which possesses the advantage of tools for numerically exact computation of steady state probability distribution, without requiring simulation. Here, we extend this model to incorporate both random sequential and parallel update rules, and demonstrate its effectiveness in various settings, including its flexibility in accommodating additional static and dynamic biological complexities and its role in parameter sensitivity analysis. In these applications, the results from the model analysis match those of TASEP model simulations. Importantly, the proposed modelling framework maintains the stochastic aspects of mRNA translation and provides a way to exactly calculate probability distributions, providing additional tools of analysis in this context. Finally, the proposed modelling methodology provides an alternative approach to the understanding of the mRNA translation process, by bridging the gap between existing approaches, providing new analysis tools, and contributing to a more robust platform for modelling and understanding translation.

Conference Articles

1. Improved Results on Stability of Markovian Jump Systems with Time-Varying Delays Pengfei Li, Yu Kang, and Yun-Bo Zhao In IEEE International Conference on Control Applications 2016 [Abs] [doi] [pdf]

   The stochastic stability of Markovian jump sys- tems with time-varying delays is investigated. Three delay- partitioning methods, i.e., partitioning [0,dmin], [dmin,dmax] and both, are considered to derive the delay-range-dependent stability criteria. Different Lyapunov-Krasovskii functionals are consequently constructed for the stochastic stability of the sys- tem. The effectiveness of those results are finally domonstrated by a numerical example.

2. Location Problem for Traffic Emission Monitors Zerui Li, Yu Kang, and Yun-Bo Zhao In International Conference on Human System Interactions 2016 [Abs] [doi] [pdf]

   In order to mitigate the air pollution caused by traf- fic, the monitoring of on-road vehicle emission is really an urgent issue. The Vehicle Emission Remote Sensing System (VERSS) is a promising technology to solve this problem. But there is scarcely any available location strategy for traffic emission monitors yet to our knowledge, which restraints the use of monitors on a large scale of traffic network. In this paper, we make some efforts to solve a novel location problem in the transportation domain, that is, we look for the minimum subset of roads on which traffic emission monitors should be located, thus we can detect as many on-road vehicles as possible. We explicate how to transform the location problem to some graph problems and give the problem formulation mathematically. Then a two-step algorithm is designed to find the set of roads to locate monitors. The simulation test verify its availability. And in the last section some problems that should be studied further are presented at the end of the paper.

3. Remote Sensing and Artificial Neural Network Estimation of On-Road Vehicle Emissions Zerui Li, Yu Kang, and Yun-Bo Zhao In International Conference on Advanced Information Networking and Applications 2016 [Abs] [doi] [pdf]

   The emissions of on-road vehicles are studied based on a remote sensing system and artificial neural network models. A transportable vehicle emission remote sensing system is used to collect the emission data from May to August 2012 in Hefei, China. Based on these light-duty gasoline vehicle data containing the emission pollutants such as carbon monoxide, hydrocarbons, nitric oxide, and so on, artificial neural network models are constructed to estimate the relation of fuel-based emission factors and input parameters. The performance of the developed models is analyzed and compared, showing that neural networks perform better than the multiple linear regression models, thus validating the effectiveness of neural networks in vehicle emission estimation.

4. Fusion Approach for Real-Time Mapping Street Atmospheric Pollution Concentration Wenjun Lv, Yu Kang, Zerui Li, and Yun-Bo Zhao In International Conference on Human System Interactions 2016 [Abs] [doi] [pdf]

   The real-time mapping of street atmospheric pollu- tion concentration does play an important role because its knowl- edge is crucial for strategy-makers to make more effective control strategies to decrease urban atmospheric pollution and improving urban atmospheric environment. Combining the conventional methods (e.g. the dispersion model prediction and neural network prediction) and mobile measurement technology (e.g. the GMAP vehicle) which their characteristics are complementary, a linear model is proposed and then a fusion approach called weighting filter derived from the concept of Kalman filter. Moreover, a self- tuning regulator is introduced to adjust the parameters of filter for the changing noise statistical characteristics over time which mainly caused by season switch. The performances of asymptotic stability and asymptotic optimality are both mathematically proven. Finally a simulation test is conducted to verify this approach.

2015

Journal Articles

1. Regularization and Robust Stabilization of Uncertain Descriptor Fractional-Order Systems Rong Huang, Yu Kang, and Yun-Bo Zhao Journal of Zhejiang University SCIENCE A 2015 [pdf]
2. Road Characteristic Based Location Technique for Vehicle Exhaust Gas Detection Zerui Li, Yu Kang, Xin Wang, Xiaobin Tan, and Yun-Bo Zhao IFAC-PapersOnLine 2015 [Abs] [doi] [pdf]

   Now there is a growing awareness that the air pollution in China is getting more and more serious, and the leading factor is the exhaust produced by vehicles in cities. To detect every registered vehicle enables us to take means to control the air pollution in cities. The conventional methods detecting exhaust by simulating running states, cannot capture the real-time emissions effectively. The remote sensing system for exhaust gas detection is an effective way to solve these problems. However, there is no location technique on how to place vehicle exhaust detecting devices in traffic network. This paper proposes a road characteristic based location technique for the devices. After determining the monitoring area and collecting the related road information, a model based on the road characteristics is established. Then these roads are clustered into several parts and the core roads will be the location. Finally the several schemes are evaluated, ranked and picked to achieve a relatively optimal location scheme.

3. Stability Analysis and Stabilization of a Class of Cutting Systems with Chatter Suppression Hao-Fei Meng, Yu Kang, Zhiyong Chen, and Yun-Bo Zhao IEEE/ASME Transactions on Mechatronics 2015 [Abs] [doi] [pdf]

   In this paper, a new method for stability analysis of a single degree of freedom (SDOF) cutting system with sinusoidal spindle speed variation (SSSV) is proposed. Based on the approximately periodic prop- erty of the time delay in the turning process and the delay decomposition method, novel criteria of stability analysis for an SDOF cutting system are presented. Moreover, a state feedback controller is designed to stabilize the system and improve the steady-state response. Numerical simulation shows the effectiveness of the method.

4. Power Measurement Based on VSLMS Improved Adaptive Filter Qi Qian, Yu Kang, and Yun-Bo Zhao Journal of University of Science and Technology of China 2015 [Abs] [doi] [pdf]

   A new algorithm is proposed to detect and extract in real-time signals with fundamental and harmonic wave components in the power grid，which is applicable to power measurement. The proposed method is based on the concept of adaptive filter，and adaptively decomposes the measured power signal into its constituting components，resulting in a fast convergence rate.The fundamental and harmonic wave components in the power grid can be decomposed into a series of sinusoidal signals.The frequency of the power grid is measured by energy operator.A model of voltage and curent wave in the power grid is constructed，a new step-size LMS algorithm for improving the adaptive filter is proposed and the stability of the proposed method is discused. The efectivenes of the proposed method is demonstrated by simulation examples.

5. New Advances in Distributed Control of Large-Scale Systems Dan Zhang, Wen-An Zhang, Zheng-Guang Wu, Kun Liu, Hui Zhang, and Yun-Bo Zhao Mathematical Problems in Engineering 2015 [doi] [pdf]
6. Networked Control Systems: The Communication Basics and Control Methodologies Yun-Bo Zhao , Xi-Ming Sun, Jinhui Zhang, and Peng Shi Mathematical Problems in Engineering 2015 [Abs] [doi] [pdf]

   As an emerging research field, networked control systems have shown the increasing importance and attracted more and more attention in the recent years. The integration of control and communication in networked control systems has made the design and analysis of such systems a great theoretical challenge for conventional control theory. Such an integration also makes the implementation of networked control systems a necessary intermediate step towards the final convergence of control, communication, and computation. We here introduce the basics of networked control systems and then describe the state-of-the- art research in this field. We hope such a brief tutorial can be useful to inspire further development of networked control systems in both theory and potential applications.

7. Recent Advances on the Theory and Applications of Networked Control Systems Yun-Bo Zhao , Xi-Ming Sun, Jinhui Zhang, and Peng Shi Mathematical Problems in Engineering 2015 [doi] [pdf]

2014

Journal Articles

1. Stability Analysis of A Class of Hybrid Stochastic Retarded Systems under Asynchronous Switching Yu Kang, Di-Hua Zhai, Guo-Ping Liu, Yun-Bo Zhao , and Ping Zhao IEEE Transactions on Automatic Control 2014 [Abs] [doi] [pdf]

   The stability of a class of hybrid stochastic retarded systems (HSRSs) with an asynchronous switching controller is investigated. In this model, the controller design relies on the observed jumping parameters, which are however delayed and thus can not be measured in real-time precisely. This delayed time interval, referred to as the “asynchronous switching interval”, is Markovian and dependent on the actual switching signal. The suf- ficient conditions under which the system is either stochastically asymptotic stable or input-to-state stable are obtained by apply- ing the extended Razumikhin-type theorem to the asynchronous switching interval. These results are less conservative as it is only required that the designed Lyapunov function is non-decreasing. It is shown that the stability of the considered system can be guaranteed by a sufficiently small mode transition rate of the underlying Markov process, which is a conclusion similar to that in asynchronous deterministic switched systems. The effectiveness and correctness of the obtained results are finally verified by a numerical example.

2. Distributed \∞Consensus Filtering with Sensor Networks: A Finite Horizon Solution Wei-Ke Shang, Yu Kang, Yuanqing Xia, and Yun-Bo Zhao IMA J. Math. Control I. 2014 [Abs] [doi] [pdf]

   The distributed H\ınfty\-consensus filtering problem is investigated for a class of discrete time-varying non- linear systems on a finite horizon. The topology of the sensor networks is assumed to be Markovian switching and the missing measurements (packet dropouts) problem is also considered. Based on the recursive linear matrix inequalities, an effective distributed H\ınfty\-consensus filter is designed, which is suitable for online computation. A numerical example is given to verify the effectiveness of the obtained results.

3. mRNA Translation and Protein Synthesis: An Analysis of Different Modelling Methodologies and a New PBN Based Approach Yun-Bo Zhao , and J Krishnan BMC Syst. Biol. 2014 [Abs] [doi] [pdf]

   Conclusions The codon-based models are based on different levels of abstraction. Our analysis suggests that a multiple model approach to understanding translation allows one to ascertain which aspects of the conclusions are robust with respect to the choice of modelling methodology, and when (and why) important differences may arise. This approach also allows for an optimal use of analysis tools, which is especially important when additional complexities or regulatory mechanisms are included. This approach can provide a robust platform for dissecting translation, and results in an improved predictive framework for applications in systems and synthetic biology.

Conference Articles

1. Fuzzy-Logic Based Adaptive Weighting Filter for Strap-down Inertial Navigation Systems Wenjun Lv, Yu Kang, Zhi-Jun Li, and Yun-Bo Zhao In World Congress on Intelligent Control and Automation 2014 [Abs] [doi] [pdf]

   Strap-down inertial navigation systems are widely applicable in both military and civil industries. The outputs of gyroscope and accelerometer are polluted by noises. Different form the conventional technologies, the weighting filter pre- sented in this work is superior to both the complementary filter and Kalman filter. Furthermore, to eliminate the negative effects brought by the variation of noise statistical characteristics, a fuzzy-logic based regulator is introduced to adjust the weight coefficient adaptively. Finally, experiments are conducted to validate the efficiency of the filter.

2. Networked Predictive Control of Hammerstein Systems Tian-Tian Wang, Yu Kang, and Yun-Bo Zhao In Chinese Control Conference 2014 [Abs] [doi] [pdf]

   This article mainly deals with the control and stability problems of networked Hammerstein with nonlinear input. A novel predictive controller design method is proposed to offset the effect of network delay and data dropout. The controller gain which depends on the time delay of the feedback channel is time-variant. Since we assume that the state is not measurable, the control signal is based on the state estimated by the observer. As for the nonlinear part of the input,We assume it satisfies a sector constraint and treat it as a input inaccuracy. Theoretical results are presented for the closed-loop stability by modeling the system as time-delay Hammerstein system with nonlinear inputs. A second-order Hammerstein system is implemented to show the enhanced performance of this control method.

Book Chapters

1. Packet-Based Communication and Control Co-Design for Networked Control Systems Yun-Bo Zhao , and Guo-Ping Liu In Frontiers Of Intelligent Control And Information Processing 2014 [Abs] [doi] [pdf]

   A packet-based communication and control co-design framework is pro- posed for Networked Control Systems (NCSs). This framework takes advantage of the characteristic of the packet-based transmission in the networked control en- vironment, which enables a sequence of control signals to be sent over the net- work simultaneously within one data packet. This consequently makes it possible to actively compensate for the communication constraints in NCSs with specially designed compensation mechanisms, which can not be achieved by conventional control approaches. These compensated communication constraints include all the major ones brought by the communication network to NCSs, i.e., the network- induced delay, data packet dropout and data packet disorder, thus making the packet-based co-design approach a unified framework for NCSs. Following the de- sign of the packet-based framework, the resulting control system is mathematically formulated, its closed-loop stability is analyzed, and a receding horizon controller is designed to implement the scheme. Finally, the effectiveness of the co-design scheme is verified by numerical examples as well as an Internet-based test rig. It is believed that this packet-based communication and control co-design framework is an im- portant step towards the convergence of control, communication and computation in the new era of the information technology.

2013

Conference Articles

1. Kalman Filter-based Identification of Systems with Randomly Missing Measurements and Linear Constraints Yu Kang, Jianfei Hang, Yun-Bo Zhao , and Guo-Ping Liu In IFAC International Conference on Intelligent Control and Automation Science 2013 [Abs] [doi] [pdf]

   The available information of linear constraint in linear dynamic systems, which is often unexplored in previous works, is taken advantage of to improve the accuracy of the parameter estimation, particularly in the presence of randomly missing measurements. Specifically, a Kalman filter-based identification for systems without constraint but with the randomly missing measurements is first introduced. Then the result is extended to systems with linear constraint under normal conditions. By doing so we show that the accuracy of the estimation is improved by taking the constraint into account, both theoretically and numerically.

2. Stability of a Class of Markovian Jumping Nonlinear Systems with Time-Delay in Detection of Switching Signal Di-Hua Zhai, Yu Kang, Guo-Ping Liu, and Yun-Bo Zhao In 中国自动化学会青年学术年会 2013 [Abs] [pdf]

   The asynchronous stability of a class of Markovian jumping nonlinear systems with time- delay in detection of switching signal is investigated. In the model, the detection delay is modeled Markovian and dependent on the actual Markovian jump signal. By constructing the constraint relationships between instability margin or detection delay and the mode transition rate of the underlying Markov process, the sufficient criterion for pth moment exponentially stable is obtained. It is shown that the stability of the considered system can be guaranteed by a sufficiently small mode transition rate. Finally, a numerical example illustrates the effectiveness of the obtained results.

3. Matrix Representation of Kauffman Networks Yun-Bo Zhao , and Jongrae Kim In Chinese Control Conference 2013 [Abs] [pdf]

   Kauffman networks are a class of Boolean networks where each node has the same number of incoming connections. Despite the simplicity of such networks, they exhibit very complex behaviors and have been shown to be an appropriate model for certain gene regulatory networks. Kauffman networks are typically represented by Boolean logics for which no efficient analytical tools are available. The logical representation of Kauffman networks makes it extremely difficult to analyze their dynamic behaviors. Based on a recently developed tool named “semi-tensor product” for matrices, we propose a novel matrix representation for Kauffman networks. This matrix representation is essentially a linear discrete dynamic system, making it possible to analyze the dynamic behaviors of Kauffman networks using existing tools in dynamic systems. As an example of the advantages of using this matrix representation, we show how the number and length of attractors can be calculated efficiently which is an impossible task for the original logical representation. Some general properties of Kauffman networks are also discussed based on their matrix representation.

Journal Articles

1. Design, Analysis and Real-Time Implementation of Networked Predictive Control Systems Guo-Ping LIU, Jian Sun, and Yun-Bo Zhao Zidonghua Xuebao/Acta Automatica Sinica 2013 [Abs] [doi] [pdf]

   Abstract This paper is concerned with the design, analysis and real-time implementation of networked control systems using the predictive control strategy. The analysis of the characteristics of networked control systems is detailed, which shows that a networked control system is much different from conventional control systems. To achieve the desired performance of closed-loop networked control systems, the networked predictive control scheme is introduced. The design, stability analysis and real-time implementation of

2. On the Delay Effects of Different Channels in Internet-Based Networked Control Systems Yun-Bo Zhao , Jongrae Kim, Xi-Ming Sun, and Guo-Ping Liu Int. J. Syst. Sci. 2013 [Abs] [doi] [pdf]

   The sensor-to-controller and the controller-to-actuator delays in networked control systems (NCSs) are investigated for the first time with respect to their different effects on the system performance. This study starts with identifying the delay-independent and delay-dependent control laws in NCSs, and confirms that only two delay-dependent control laws can cause different delay effects in different channels. The conditions under which the different delays in different channels can cause different effects are then given for both delay-dependent control laws. The results are verified by numerical examples. Potentially, these results can be regarded as important design principles in the practical implementation of NCSs.

2012

Conference Articles

1. Approximation of Boolean Networks Daizhan Cheng, Yin Zhao, Jongrae Kim, and Yun-Bo Zhao In World Congress on Intelligent Control and Automation 2012 [Abs] [pdf]

   The problem of approximation to large-scale Boolean networks is considered. First, we assume a large- scale Boolean network is aggregated into several sub-networks. Using the outputs(or inputs) of each sub-network as new state variables, a new simplified time-varying network is obtained. Then a time-invariant Boolean network is used to approximate each subsystem. Observed data are used to find the best approximating dynamic models. Finally, the aggregation method is investigated.

Journal Articles

1. Stability Analysis of Nonlinear Switched Networked Control Systems with Periodical Packet Dropouts Di Wu, Xi-Ming Sun, Yun-Bo Zhao , and Wei Wang Circuits, Systems, and Signal Processing 2012 [Abs] [doi] [pdf]

   The input-to-state stability problem of a class of nonlinear switched networked control systems subject to time-varying transmission intervals, periodical packet dropouts, and communication...

2. Stability of a Class of Switched Stochastic Nonlinear Systems under Asynchronous Switching Di-Hua Zhai, Yu Kang, Ping Zhao, and Yun-Bo Zhao International Journal of Control, Automation and Systems 2012 [Abs] [pdf]

   The stability of a class of switched stochastic nonlinear retarded systems with asynchronous switching controller is investigated. By constructing a virtual switching signal and using the average dwell time approach incorporated with Razumikhin-type theorem, the sufficient criteria for pth mo- ment exponential stability and global asymptotic stability in probability are given. It is shown that the stability of the asynchronous stochastic systems can be guaranteed provided that the average dwell time is sufficiently large and the mismatched time between the controller and the systems is sufficient- ly small. This result is then applied to a class of switched stochastic nonlinear delay systems where the controller is designed with both state and switching delays. A numerical example illustrates the effec- tiveness of the obtained results.

3. Compensation and Stochastic Modeling of Discrete-Time Networked Control Systems with Data Packet Disorder Yun-Bo Zhao , Jongrae Kim, Guo-Ping Liu, and David Rees International Journal of Control, Automation and Systems 2012 [Abs] [doi] [pdf]

   Data packet disorder is inevitable in discrete-time networked control systems, which however has been ignored in most literature to date. This work investigates the cause and the negative effects of...

4. Offline Model Predictive Control-Based Gain Scheduling for Networked Control Systems Yun-Bo Zhao , Guo-Ping Liu, and J Kim IET Control Theory & Applications 2012 [Abs] [doi] [pdf]

   A control structure is investigated where the sensor is remotely located from the plant through the communication network, motivated by the inclusion of sensor fusion in networked control systems (NSCs). This control structure admits a broad class of practical NSCs but has rarely been touched before. Motivated by the packet-based control approach, the authors propose an offline model predictive control-based gain scheduling scheme for this control structure. This scheme is capable of actively compensating for the communication constraints, which is an impossible task for conventional control approaches, and at the same time it dramatically reduces the communication and computational costs compared with the packet-based control approach. In this sense, this scheme can be regarded as an important step towards the effective convergence of control, communication and computation.

2011

Conference Articles

1. Model-Based Compensation for Multi-Packet Transmission in Networked Control Systems Yun-Bo Zhao , Jongrae Kim, Guang-Hong Yang, and Guo-Ping Liu In IEEE Conference on Decision and Control 2011 [Abs] [doi] [pdf]

   The sensing data is usually transmitted simultane- ously from the sensor to the controller in conventional control systems. However, in networked control systems it is possible that a set of sensing data is transmitted via multiple separate data packets due to the multiple, geographically dispersed sensors. This scenario, referred to as “multi-packet transmission”, brings to the system different delays for different parts of the sensing data. Within the packet-based control framework for networked control systems, a novel control structure is proposed. The negative effects of multi-packet transmission are effectively dealt with by first reconstructing the sensing data at the controller side and then compensating for the communication constraints using the packet-based control approach. Numerical examples illustrate the effectiveness of the proposed approach.

2. LFT-Free \μ\-Analysis of LTI/LPTV Systems Yun-Bo Zhao , Jongrae Kim, and Declan G Bates In IEEE International Symposium on Computer-Aided Control System Design (CACSD) 2011 [Abs] [doi] [pdf]

   μ-analysis has for many years been the de facto standard robustness analysis tool in a wide variety of control applications. To use μ-analysis, the uncertain system must be cast in the form of a linear fractional transformation (LFT). Once it is appropriately transformed, a variety of algorithms are available to calculate the upper and the lower bounds on μ. Several difficulties arise during this process. Firstly, the uncertainties in the system must appear as polynomial fractions - if they do not, then some approximation steps must be applied in order to write the system as an LFT. Secondly, symbolic manipulations are required in general in order to obtain linear fractional transformation automatically. Finally, for problems involving real parametric uncertainties calculating the lower bound is a well known NP(Non-deterministic Polynomial- time)-hard problem. Therefore, there will always be some conservatism introduced in the bound or else the computation time will increase exponentially with the number of uncertain parameters. We present an efficient algorithm to overcome these issues by combining a randomisation approach and a geometric interpretation of the robustness analysis problem. The uncertainty is re-defined by a subtraction between the uncertain system and the nominal system. Thus the procedure does not require that the uncertain parameters are actually decoupled from the system (as with LFT’s) but only requires the evaluation of the difference between the nominal system and the perturbed system. Here, we illustrate the application of the proposed approach to the robustness analysis of uncertain linear periodically time-varying systems, and in particular to magnetic torquer controlled spacecraft attitude dynamics.

3. PDV-Based Packet Length Allocation for Networked Control Systems Yun-Bo Zhao , Jongrae Kim, Peng Shi, and Guo-Ping Liu In International Conference on Intelligent Control and Information Processing 2011 [Abs] [doi] [pdf]

   Networked control systems typically involve mul- tiple subsystems which share the communication network. In this system setting, besides the optimization of the quality of control of the considered networked control system, the efficient utilization of the communication resources contributes to part of the overall control objective. By realizing the fact that the packet delay variation can be roughly piecewise constant in a relatively large system, a packet length allocation scheme is proposed for this system setting. With the use of this scheme, the performance of the networked control system can be maintained at an acceptable level at the dramatically reduced cost of the communication resources. As a result an optimized balance be- tween the quality of control of the considered networked control system and the quality of service of the communication network can thus be achieved. Simulations illustrate the effectiveness of the proposed scheme.

4. Simplified Algorithm and Framework for Networked Predictive Control Systems Yun-Bo Zhao , Jongrae Kim, and Guo-Ping Liu In Chinese Control Conference 2011 [Abs] [pdf]

   The packet-based control approach has proven to be a promising method to deal with the communication constraints in networked control systems. Within this framework, model predictive control is often used to design the packet-based controller due to its favored control structure. In this work we discover an implicit relationship of the feedback gains obtained using the model predictive control method between networked predictive control systems and conventional control systems. This relationship is shown to be effective in simplifying the algorithm as well as the framework of the original networked predictive control system structure, and thus is of importance in the implementation of networked predictive control systems.

Journal Articles

1. Error Bounded Sensing for Packet-Based Networked Control Systems Yun-Bo Zhao , Jongrae Kim, and Guo-Ping Liu IEEE Transactions on Industrial Electronics 2011 [Abs] [doi] [pdf]

   An error bounded sensing strategy is proposed within the packet-based control framework for networked control systems (NCSs). This strategy reduces the data transmissions in both the sensor-to-controller and the controller-to-actuator chan- nels by allowing the transmissions of only the sensing and control data packets that satisfy some predetermined transmission rules. By fitting it into the packet-based control framework for NCSs, this strategy can achieve the goal of reducing the use of the communication resources while at the same time maintaining the system performance at an acceptable level. Stabilized controllers are designed within this framework, and the effects on the system stability brought by this approach are also investigated. Numer- ical and experimental examples illustrate the effectiveness of the proposed approach.

2. Stochastic Stabilization of Packet-Based Networked Control Systems Yun-Bo Zhao , Yu Kang, Guo-Ping Liu, and David Rees International Journal of Innovative Computing, Information and Control 2011 [Abs] [pdf]

   A packet-based control approach is proposed for networked control systems (NCSs). This approach takes advantage of the packet-based transmission of the network in NCSs and as a consequence the control law can be designed with explicit compensa- tion for the network-induced delay, data packet dropout and data packet disorder in both forward and backward channels. Under the Markov chain assumption of the network- induced delay (data packet dropout as well), the sufficient and necessary conditions for the stochastic stability and stabilization of the closed-loop system are obtained. A nu- merical example illustrates the effectiveness of the proposed approach.

2010

Journal Articles

1. H∞ Control for Networked Predictive Control Systems Based on the Switched Lyapunov Function Method Rui Wang, Guo-Ping Liu, Wei Wang, David Rees, and Yun-Bo Zhao IEEE Transactions on Industrial Electronics 2010 [Abs] [doi] [pdf]

   This paper investigates the problem of H\ınfty control for a class of networked control systems (NCSs) with time-varying delay in both forward and backward channels. Combined with the switched Lyapunov function technique, an improved predictive controller design strategy is proposed to compensate for the delay and data dropout to achieve the desired control performance. Based on these methods, the controllers can be designed to guar- antee that the closed-loop system is asymptotically stable with an H\ınfty\-norm bound in terms of nonlinear matrix inequalities. An iterative algorithm is presented to solve these nonlinear matrix inequalities to obtain a suboptimal minimum disturbance attenu- ation level. Numerical simulations and a practical experiment are given to illustrate the effectiveness of the proposed method.

2. Guaranteed Cost Control for Networked Control Systems Based on an Improved Predictive Control Method Rui Wang, Guo-Ping Liu, Wei Wang, David Rees, and Yun-Bo Zhao IEEE Transactions on Control Systems Technology 2010 [Abs] [doi] [pdf]

   This brief deals with the problem of guaranteed cost control for a class of uncertain networked control systems with time-varying delay. An improved predictive controller design strategy is proposed to compensate for the delay and data dropout in both the forward and backward channels to achieve the de- sired control performance. The varying controller gains which are designed to vary with delays can lead to less conservative results. Meanwhile, an algorithm involving a convex optimization problem is presented to achieve a suboptimal guaranteed cost. Furthermore, a numerical simulation and a practical experiment are given to illustrate the effectiveness of the proposed method.

3. Actively Compensating for Data Packet Disorder in Networked Control Systems Yun-Bo Zhao , Guo-Ping Liu, and David Rees IEEE Transactions on Circuits and Systems II: Express Briefs 2010 [Abs] [doi] [pdf]

   Data packet disorder often occurs in networked con- trol systems (NCSs), which, however, has not been taken into account in most literature to date. In this brief, the cause and effect of data packet disorder are analyzed, and an active compensation scheme is proposed to compensate for it. The proposed scheme is flexible to admit all the existing control approaches to be used and also derives a novel closed-loop system model of NCSs, which en- ables more reasonable and effective theoretical analysis of NCSs. The effectiveness of the proposed active compensation scheme is illustrated by a numerical example.

4. Packet-Based Deadband Control for Internet-Based Networked Control Systems Yun-Bo Zhao , Guo-Ping Liu, and David Rees IEEE Transactions on Control Systems Technology 2010 [Abs] [doi] [pdf]

   A packet-based deadband control approach is pro- posed for networked control systems (NCSs). Compared with previously reported packet-based control approaches to NCSs, the approach proposed in this paper takes full advantage of the packet-based data transmission in NCSs, and thus considerably reduces the use of the communication resources in NCSs whilst maintaining the system performance at a satisfactory level. A stabilized controller design method is obtained using time delay switched system theory, which has not been achieved in previ- ously reported packet-based control approaches. The proposed deadband control strategy and the stabilized controller design method are verified using a numerical example as well as practical experiments based on an Internet-based test rig for NCSs.

5. Stability and Stabilisation of Discrete-Time Networked Control Systems: A New Time Delay System Approach Yun-Bo Zhao , Guo-Ping Liu, and D Rees IET Control Theory & Applications 2010 [Abs] [doi] [pdf]

   A large number of research works on networked control systems (NCSs) are from the time delay system (TDS) perspective, however, it is noticed that the description of the network-induced delay is too general to represent the practical reality. By recognising this fact, a novel TDS model for NCSs is thus obtained by depicting the network-induced delay more specifically. Based on this model, stability (robust stability) and stabilisation results are obtained using delay-dependent analysis approach, which are less conservative compared with conventional models because of the specific description of the network-induced delay in the new model. A numerical example illustrates the effectiveness of the proposed approach.

Conference Articles

1. Comparing the Delay Effects in Different Channels in Packet-Based Networked Control Systems Yun-Bo Zhao , Jongrae Kim, and Guo-Ping Liu In International Conference on Intelligent Control and Information Processing 2010 [Abs] [doi] [pdf]

   The different effects of the sensor-to-controller and the controller-to-actuator delays in networked control systems, are investigated within the packet-based control framework. The study starts with identifying the specific control strategies that make those two delays different for the system. The problem is then carefully formulated and theoretical analysis is conducted, revealing that under certain conditions the sensor- to-controller delay can cause less deterioration of the system performance than the controller-to-actuator delay. This result is verified by a numerical example and has its practical guidance value.

2. Hybrid Moment Computation Algorithm for Biochemical Reaction Networks Yun-Bo Zhao , Jongrae Kim, and João Pedro Hespanha In IEEE Conference on Decision and Control 2010 [Abs] [doi] [pdf]

   Moment computation is essential to the analysis of stochastic kinetic models of biochemical reaction networks. It is often the case that the moment evolution, usually the first and the second moment evolutions over time, is all the information of interest. However, potential approaches to moment compu- tation, specifically, the moment closure method and the exact stochastic simulation method, have their significant deficiency. The former, despite its computational efficiency, is essentially an approximation to the real solution and thus is lack of inaccuracy at certain conditions, while the computational inefficiency makes the usage of the latter limited to the networks with small number of molecules. A hybrid moment computation algorithm is therefore proposed by integrating the moment closure method and the exact stochastic simulation algorithms. The moment closure method and the stochastic simulation algorithm operate by turns to achieve an optimal balance between the efficiency due to the moment closure method and the accuracy due to the stochastic simulation. The hybrid algorithm is applied to a Dictyostelium cAMP oscillation network. The simulation results illustrate the effectiveness of the algorithm.

2009

Journal Articles

1. Design of a Packet-Based Control Framework for Networked Control Systems Yun-Bo Zhao , Guo-Ping Liu, and D Rees IEEE Transactions on Control Systems Technology 2009 [Abs] [doi] [pdf]

   A packet-based control framework is proposed for networked control systems (NCSs). This framework takes ad- vantage of the characteristic of the packet-based transmission in a networked control environment, which enables a sequence of control signals to be sent over the network simultaneously, thus making it possible to actively compensate for the communication constraints in NCSs. Under this control framework and a deriving delay-dependent feedback gain scheme, a novel model for NCSs is proposed which can deal with network-induced delay, data packet dropout and data packet disorder in NCSs simultaneously and a receding horizon controller is also designed to implement the packet-based control approach. This approach is then verified by a numerical example and furthermore an Internet-based test rig which illustrates the effectiveness of the proposed approach.

2. Modeling and Stabilization of Continuous-Time Packet-Based Networked Control Systems Yun-Bo Zhao , Guo-Ping Liu, and David Rees IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics 2009 [Abs] [doi] [pdf]

   In this paper, the packet-based control approach to networked control systems (NCSs) is extended to the continuous-time case with the use of a discretization technique for continuous network-induced delay. The derived approach can effectively simultaneously deal with network-induced delay, data packet dropout, and data packet disorder and leads to a novel model for NCSs. This model offers the designer the freedom of designing different controllers with respect to specific network conditions, which is distinct from previous results and ensues better system performance. By applying switched system theory, the stability criterion for the derived model is obtained, which is then used to obtain an linear matrix inequality-based stabilized controller design method for the packet-based control approach. A numerical example is also presented, which illustrates the effectiveness of the proposed packet-based control approach by comparison.

Conference Articles

1. Stochastic Stability Analysis of Packet-Based Networked Control Systems Yun-Bo Zhao , Guo-Ping Liu, and David Rees In IEEE Conference on Decision and Control 2009 [Abs] [doi] [pdf]

   By taking advantage of the packet-based transmis- sion in networked control systems (NCSs), a packet-based control approach is proposed for NCSs. Using this approach, the control law can be designed with explicit compensation for network- induced delay, data packet dropout and data packet disorder simultaneously. The sufficient and necessary condition for the stochastic stability of the closed-loop system is obtained, by modeling the closed-loop system as a Markov jump system. A numerical example is also considered to illustrate the effectiveness of the proposed approach.

2. Using Deadband in Packet-Based Networked Control Systems Yun-Bo Zhao , Guo-Ping Liu, and David Rees In IEEE International Conference on Systems, Man and Cybernetics 2009 [Abs] [doi] [pdf]

   A packet-based deadband control approach is pro- posed for Networked Control Systems (NCSs). Within the packet- based control framework for NCSs, the proposed deadband control strategy takes full advantage of the packet-based data transmission in NCSs, and thus considerably reduces the use of the communication resources in NCSs whilst maintaining the system performance at a satisfactory level. The stability conditions of the closed-loop system are obtained and a numerical example illustrating the effectiveness of the proposed approach is presented.

2008

Conference Articles

1. Design and Stability Analysis of Packet-Based Networked Control Systems in Continuous Time Yun-Bo Zhao , Guo-Ping Liu, and David Rees In IEEE International Conference on Systems, Man and Cybernetics 2008 [Abs] [doi] [pdf]

   In this paper, the packet-based control approach to networked control systems is extended to the continuous time case, with the use of a discretization technique for the continuous network-induced delay. The derived approach can effectively deal with network-induced delay, data packet dropout and data packet disorder simultaneously, and also leads to a novel model for networked control systems. This model offers the designer the freedom of designing different controllers with respect to specific network conditions, which is distinct from previous results and is expected to result in better performance. By applying switched system theory, the stability criterion for the closed-loop system is obtained. A numerical example to illustrate the effectiveness of the proposed approach is also presented.

Journal Articles

1. Improved Predictive Control Approach to Networked Control Systems Yun-Bo Zhao , D Rees, and Guo-Ping Liu IET Control Theory and Applications 2008 [Abs] [doi] [pdf]

   A predictive control-based approach is proposed to networked control systems. In this approach, an improved predictive controller is designed using delayed sensing data and a compensation scheme is proposed to overcome the negative effects of the network-induced delays and data packet dropouts in both the forward and backward channels. The proposed approach is easy to be implemented in practice compared with previous results in that only delayed data of the control inputs are used to derive the forward control predictions. The stability of the closed-loop system is obtained by modelling the system as a time delay system with structural uncertainties. Simulations show that the proposed approach is superior to the previous results in the situation where only delayed data are used.

2. Integrated Predictive Control and Scheduling Co-Design for Networked Control Systems Yun-Bo Zhao , D Rees, and Guo-Ping Liu IET Control Theory and Applications 2008 [Abs] [doi] [pdf]

   A predictive control and scheduling co-design approach is proposed to deal with the con- troller and scheduler design for a set of networked control systems which are connected to a shared communication network. In the proposed approach, a predictive controller is applied to generate the control predictions for each system using delayed sensing data and previous control infor- mation, and a time delay compensator is designed at the actuator side to actively compensate for the network-induced delay in the forward channel when the control action is taken. Two differ- ent scheduling algorithms, the existing static rate monotonic (RM) scheduling algorithm and a new dynamic scheduling algorithm called dynamic feedback scheduling (DFS), are considered to sche- dule the transmissions of the control signals generated by the predictive controller, which are packed and transmitted to the actuator in one packet simultaneously. Both the scheduling algor- ithms are designed with the guarantee of the stability of all the systems, which is achieved by ensur- ing that the time delay of the systems do not exceed the upper bound under which the systems are stable. It is also pointed out that the RM algorithm is a special case of the proposed DFS algorithm, in the sense that the former can work only in a private network environment, whereas the latter extends its application to such networks where other components occupying the network. Simulations for both the RM and the DFS algorithms, illustrate the validity of the proposed approach.

3. Networked Predictive Control Systems Based on the Hammerstein Model Yun-Bo Zhao , Guo-Ping Liu, and D Rees IEEE Transactions on Circuits and Systems II: Express Briefs 2008 [Abs] [doi] [pdf]

   In this paper, a novel predictive control-based ap- proach is proposed for a networked control system with random delays containing an input nonlinear process based on a Hammer- stein model. The method uses a time-delay two-step generalized predictive control scheme, which consists of two parts: one is to deal with the input nonlinearity of the Hammerstein model and the other is to compensate for the network-induced delay in the networked control system. A theoretical result using the Popov criterion is presented for the closed-loop stability of the system in the case of a constant delay. Simulation examples illustrating the validity of the approach are also presented.

4. A Predictive Control Based Approach to Networked Wiener Systems Yun-Bo Zhao , Guo-Ping Liu, and David Rees International Journal of Innovative Computing, Information and Control 2008 [Abs] [pdf]

   A predictive control based approach is proposed to deal with a Wiener type system which is closed through a network. In this approach, an output feedback predictive controller is designed using delayed sensing data with a specially designed state observer. The network constraints, i.e., the network-induced delay and data packet dropout, are compensated in both the forward and backward channels by taking advantage of the char- acteristics of both the predictive controller and the network transmission. Stability of the closed-loop system is derived by using the separation principle and switched system theory. Simulations illustrate the validity of the proposed approach.

5. A Predictive Control-Based Approach to Networked Hammerstein Systems: Design and Stability Analysis Yun-Bo Zhao , Guo-Ping Liu, and David Rees IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society 2008 [Abs] [doi] [pdf]

   In this paper, a predictive control-based approach is proposed for a Hammerstein-type system which is closed through some form of network. The approach uses a two-step predictive controller to deal with the static input nonlinearity of the Hammerstein system and a delay and dropout compensation scheme to compensate for the communication constraints in a networked control environment. Theoretical results are presented for the closed-loop stability of the system. Simulation examples illustrating the validity of the approach are also presented.

2007

Conference Articles

1. A Predictive Control Based Approach to Networked Control Systems with Input Nonlinearity: Design and Stability Analysis Yun-Bo Zhao , Guo-Ping Liu, and David Rees In International Conference on Automation and Computing 2007 [Abs] [pdf]

   In this paper, a predictive control based approach is proposed for a networked control system containing a nonlinear input process. The approach uses a two-step predictive controller to deal with the input nonlinearity and a delay and dropout compensation scheme to compensate for the communication constraints in a networked control environment. Theoretical results are presented for the closed-loop stability of the system. Simulation examples illustrating the validity of the approach are also presented.

2. Time Delay Compensation and Stability Analysis of Networked Predictive Control Systems Based on Hammerstein Model Yun-Bo Zhao , Guo-Ping Liu, and David Rees In IEEE Int. Conf. Networking, Sensing and Control 2007 [Abs] [doi] [pdf]

   A novel approach is proposed for a networked control system with random delays containing a nonlinear process based on a Hammerstein model. The method uses a Time Delay Two Step Generalized Predictive Control(TDTSGPC), which consists of two parts, one is to deal with the input nonlinearity of the Hammerstein model and the other is to compensate the network induced delays in the networked control system. Theoretical results using the Popov theorem are presented for the closed-loop stability of the system in the case of a constant delay. Simulation examples illustrating the validity of the approach are presented.

2005

Conference Articles

1. Ad Hoc网络中两种功率控制策略的传输容量分析 赵云波, and 张纪峰 In 中国控制会议 2005 [Abs] [pdf]

   本文利用建立的网络模型，对ad hoc望重两种常用的功率控制策略（“同功率”策略和“分簇”策略）的传输性能进行了定量分析，分别得到了在这两种功率控制策略下网络的传输容量上界，分析了各种实际因素对网络传输容量的影响。

Journal Articles

1. 无线通信网络中的功率控制及相关控制理论问题 赵云波, and 张纪峰 自动化博览 2005 [Abs] [pdf]

   介绍了无线通信网络中的功率控制问题，分析了功率控制与传统控制问题的异同，指出了可能存在的若干控制理论问题，提出了一些可能有效的解决方法和思路。