Consensus Control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances

Qinglai Wei; Xin Wang; Xiangnan Zhong; Naiqi Wu

doi:10.1109/JAS.2021.1003838

Volume 8 Issue 2

Feb. 2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2021 > 8(2): 423-431

Qinglai Wei, Xin Wang, Xiangnan Zhong and Naiqi Wu, "Consensus Control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 423-431, Feb. 2021. doi: 10.1109/JAS.2021.1003838

Citation:

Qinglai Wei, Xin Wang, Xiangnan Zhong and Naiqi Wu, "Consensus Control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 423-431, Feb. 2021. doi: 10.1109/JAS.2021.1003838

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Consensus Control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances

doi: 10.1109/JAS.2021.1003838

Funds: This work was supported in part by the National Natural Science Foundation of China (61722312, 61533017, 62073321) and the National Key Research and Development Program of China (2018YFB1702300)

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Author Bio:
Qinglai Wei (M’11–SM’20) received the B.S. degree in automation, and the Ph.D. degree in control theory and control engineering, from Northeastern University, in 2002 and 2009, respectively. From 2009 to 2011, he was a Postdoctoral Fellow with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. He is currently a Professor of the institute and the Associate Director of the laboratory. He has authored four books, and published over 80 international journal papers. His research interests include adaptive dynamic programming, neural-networks-based control, optimal control, nonlinear systems and their industrial applications.He is the Secretary of IEEE Computational Intelligence Society (CIS) Beijing Chapter since 2015. He was guest editors for several international journals. He was a recipient of IEEE/CAA Journal of Automatica Sinica Best Paper Award, IEEE System, Man, and Cybernetics Society Andrew P. Sage Best Transactions Paper Award, IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Award, the Outstanding Paper Award of Acta Automatica Sinica, IEEE the 6th Data Driven Control and Learning Systems Conference (DDCLS2017) Best Paper Award, and Zhang Siying Outstanding Paper Award of Chinese Control and Decision Conference (CCDC). He was a recipient of Shuang-Chuang Talents in Jiangsu Province, China, Young Researcher Award of Asia Pacific Neural Network Society (APNNS), Young Scientst Award and Yang Jiachi Tech Award of Chinese Association of Automation (CAA). He is a Board of Governors (BOG) member of the International Neural Network Society (INNS) and a council member of CAA

Xin Wang received the B.S. degree in electronic information engineering from Zhengzhou University, in 2012, and the M.S. degree in control engineering from University of Science and Technology Beijing, in 2015.He is currently pursuing the Ph.D. degree in control theory and control engineering with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, and the University of Chinese Academy of Sciences, Beijing. His current research interests include reinforcement learning, adaptive dynamic programming, optimal control, and multi-agent system

Xiangnan Zhong (M’17) received the B.S. and M.S. degrees in automation, and control theory and control engineering from Northeastern University, in 2010 and 2012, respectively and the Ph.D. degree with the Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA, in 2017.She is currently an Assistant Professor with the Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA. Her current research interests include computational intelligence, reinforcement learning, cyber-physical systems, networked control systems, neural network, and optimal control. Dr. Zhong was a recipient of the International Neural Network Society Doctoral Dissertation Award in 2019. She has been actively involved in numerous conference and workshop organization committees in the society

Naiqi Wu (M’04–SM’05–F’19) received the B.S. degree in electrical engineering from Anhui University of Technology, Huainan, China, in 1982, the M.S. and Ph.D. degrees in systems engineering both from Xi’an Jiaotong University, Xi’an, China, in 1985 and 1988, respectively. From 1988 to 1995, he was with Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China and from 1995 to 1998, with Shantou University, Shantou, China. He moved to Guangdong University of Technology, Guangzhou, China in 1998. He joined Macau University of Science and Technology, Macau, China in 2013. He was a Visiting Professor at Arizona State University, Tempe, USA in 1999; New Jersey Institute of Technology, Newark, USA, in 2004; University of Technology of Troyes, Troyes, France, from 2007 to 2009; and Evry University, Evry, France, from 2010 to 2011.He is currently a Chair Professor at the Institute of Systems Engineering, Macau University of Science and Technology, Macau, China. His research interests include production planning and scheduling, manufacturing system modeling and control, discrete event systems, Petri net theory and applications, intelligent transportation systems, and energy systems. He is the author or coauthor of one book, five book chapters, and 170+ peer-reviewed journal papers. Dr. Wu was an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics, Part C, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and Editor in Chief of Industrial Engineering Journal, and is an Associate Editor of Information Sciences
Corresponding author: Q. L. Wei and X. Wang are with the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, and with the School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, and also with Qingdao Academy of Intelligent Industries, Qingdao 266109, China (e-mail: qinglai.wei@ia.ac.cn; wangxin2019@ia.ac.cn)
Received Date: 2020-09-07
Accepted Date: 2020-10-01

Available Online: 2020-10-16

Abstract

Abstract

This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown motion. Its main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic noise. In traditional ways, the coupling weights depending on the communication structure are static. A new distributed controller is designed based on Riccati inequalities, while updating the coupling weights associated with the gain matrix by state errors between adjacent agents. By introducing time-varying coupling weights into this novel control law, the state errors between leader and followers asymptotically converge to the minimum value utilizing the local interaction. Through the Lyapunov directed method and Itô formula, the stability of the closed-loop system with the proposed control law is analyzed. Two simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method.
- Consensus control,
- directed topology,
- external disturbance,
- multi-agent (MA) systems

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References

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Highlights

A new distributed control law for multi-agent system is proposed.
The system is directed and inferred by external stochastic disturbance.
The proposed control law can solve the dynamic consensus problems.

Consensus Control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances

doi: 10.1109/JAS.2021.1003838

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通讯作者: 陈斌, bchen63@163.com

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