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Distributed Functional Observer-based Event-triggered Containment Control of Multi-agent Systems

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  • Control Theory and Applications
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Abstract

This paper studies the containment control problem of linear multi-agent systems (MASs) with a directed graph, where the states of the followers will asymptotically converge to the convex hull formed by those of the leaders. By introducing the functional observer, a novel distributed event-triggered control strategy is presented to schedule communications between agents, which depends on the relative input and output measurements with its neighbors. Furthermore, the minimum inter-event time is guaranteed to be strictly positive. An example is presented to illustrate the feasibility and efficiency of the theoretic results.

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Author information

Authors and Affiliations

  1. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China

    Long Jian, Jiangping Hu & Zhinan Peng

  2. College of Electrical and Information Engineering, Southwest Minzu University, Chengdu, 610041, P. R. China

    Jun Wang

  3. School of Information Science and Engineering, Chengdu University, Chengdu, 610106, P. R. China

    Kaibo Shi

  4. Department of Electronic Engineering, University of York, York, North Yorkshire, UK

    Yaoru Yang

  5. Vanderbilt University, Nashville, USA

    Jiuke Huang

Authors
  1. Long Jian
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  2. Jiangping Hu
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  5. Zhinan Peng
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Corresponding author

Correspondence to Kaibo Shi.

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Recommended by Editor Jessie (Ju H.) Park. This work was supported by the National Natural Science Foundation of China under Grant Nos. 61703060, 61473061, 61522310, 71503206, 61104104, 11601474, 11461082, the Opening Fund of Geomathematics Key Laboratory of Sichuan Province (scsxdz2018zd02 and scsxdz2018zd04), the Fundamental Research Funds for the Central Universities, Southwest Minzu University (2019NQN07)and the Program for New Century Excellent Talents in University under Grant no. NCET-13-0091.

Long Jian received his M.S. degree in applied mathematics from Taiyuan University of Technology, Taiyuan, China, in 2014. He is currently pursuing a Ph.D. degree with the School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China. His research interests include multi-agent systems, distributed optimization, and event-triggered control.

JiangPing Hu received his B.S. degree in applied mathematics and his M.S. degree in computational mathematics from Lanzhou University, China, in 2000 and 2004, respectively, and his Ph.D. degree in complex system modeling and control from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China, in 2007. He has held various faculty/research/visiting positions at the Royal Institute of Technology (KTH), Sweden, the City University of Hong Kong, Hong Kong, Sophia University, Japan, and the University of Western Sydney, Australia. He is currently a Professor with the School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China. His current research interests include distributed control and optimization, machine learning, and sensor networks. Dr. Hu has been serving as an Associate Editor for the journal Kybernetika since 2016.

Jun Wang received her Ph.D. degree at the Key Lab of Science and Technology on Communications, University of Electronic Science and Technology of China. Now she is a lecturer at the College of Electrical and Information Engineering at the Southwest Minzu University. Her research interests include interference cancellation, network control, boolean control networks, complex network, signal processing in wireless communication.

Kaibo Shi received his Ph.D. degree in School of Automation Engineering at the University of Electronic Science and Technology of China. He is an associate professor of School of Information Sciences and Engineering, Chengdu University. From September 2014 to September 2015, he was a visiting scholar at the Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada. He was a Research Assistant with the Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, from May 2016 to Jun 2016 and January 2017 to October 2017. His current research interests include stability theorem, the robustness stability, robust control, sampled-data control, synchronization, Lurie chatic system, stochastic systems and neural networks. He is the author or coauthor of over 50 research articles. He is the editorial board member of Applied and Computational Mathematics and a very active reviewer for many international journals.

Zhinan Peng received his B.S. degree in information and computing science from Fuyang Normal University, Fuyang, China, in 2014, and his M.S. degree in computational mathematics from the University of Electronic Science and Technology of China, Chengdu, China, in 2016. He is currently pursuing a Ph.D. degree with the School of Automation Engineering, UESTC, Chengdu, China. His current research interests include neural networks based control, multi-agent systems, adaptive dynamic programming, reinforcement learning.

Yaoru Yang received his B.S degree in Communication Engineering from Communication University of China and his M.S. degree in Digital Signal Processing from University of York. He is currently pursuing a Ph.D. degree with Department of Electronic Engineering, University of York, York, North Yorkshire, UK. He is interested in topics in Statistical Signal Processing, Statistical machine learning, Artificial Neural Network and Computational Neuroscience.

Jiuke Huang is currently pursuing a B.S. degree in Computer Science and a B.A. degree in Mathematics from Vanderbilt University, Nashville, USA. Her major is Computer Science and Mathematics, Economics. She is interested in topics in Statistical Machine Learning, Natural Language Processing and Data Science.

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Jian, L., Hu, J., Wang, J. et al. Distributed Functional Observer-based Event-triggered Containment Control of Multi-agent Systems. Int. J. Control Autom. Syst. 18, 1094–1102 (2020). https://doi.org/10.1007/s12555-019-9477-x

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