Adaptive Containment Control for Fractional-Order Nonlinear Multi-Agent Systems With Time-Varying Parameters

Yang Liu; Huaguang Zhang; Yingchun Wang; Hongjing Liang

doi:10.1109/JAS.2022.105545

Volume 9 Issue 9

Sep. 2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2022 > 9(9): 1627-1638

Y. Liu, H. G. Zhang, Y. C. Wang, and H. J. Liang, “Adaptive containment control for fractional-order nonlinear multi-agent systems with time-varying parameters,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1627–1638, Sept. 2022. doi: 10.1109/JAS.2022.105545

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Y. Liu, H. G. Zhang, Y. C. Wang, and H. J. Liang, “Adaptive containment control for fractional-order nonlinear multi-agent systems with time-varying parameters,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1627–1638, Sept. 2022. doi: 10.1109/JAS.2022.105545

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Adaptive Containment Control for Fractional-Order Nonlinear Multi-Agent Systems With Time-Varying Parameters

doi: 10.1109/JAS.2022.105545

1.
College of Information Science and Engineering, Northeastern University, Shenyang 110004, China
2.
State Key Laboratory of Synthetical Automation for Process Industries, and also with the School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
3.
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: This work was supported by National Key R&D Program of China (2018YFA0702200), and National Natural Science Foundation of China (61627809, 62173080), and Liaoning Revitalization Talents Program (XLYC1801005)

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Author Bio:
Yang Liu received the B.S. degree in mathematics from Bohai University in 2009, the M.S. degree in fundamental mathematics from Northeastern University in 2011. She is currently pursuing the Ph.D. degree in control theory and control engineering at Northeastern University. Her current research interests include adaptive fuzzy control, neural-networks control

Huaguang Zhang (Fellow, IEEE) received the B.S. and M.S. degrees in control engineering from the Northeast Dianli University of China in 1982 and 1985, respectively, and the Ph.D. degree in thermal power engineering and automation from Southeast University in 1991. He joined the Department of Automatic Control, Northeastern University in 1992, as a Post-Doctoral Fellow, for two years, where he has been a Professor and the Head of the Institute of Electric Automation, College of Information Science and Engineering, since 1994. He has authored or co-authored over 200 journal and conference papers and four monographs and has co-invented 20 patents. His current research interests include fuzzy control, stochastic-system control, neural-network-based control, nonlinear control, and their applications. He was a recipient of the Outstanding Youth Science Foundation Award from the National Natural Science Foundation Committee of China in 2003. He was named the Cheung Kong Scholar by the Education Ministry of China in 2005. He is an Associate Editor of Automatica, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics

Yingchun Wang (Member, IEEE) received the B.S., M.S., and Ph.D. degrees from Northeastern University in 1997, 2003, and 2006, respectively. He is currently an Associate Professor with the College of Information Science and Engineering, Northeastern University. From 2015 to 2016, he was a Visiting Scholar with West Sydney University, Australia. He was an Outstanding Reviewer of IEEE Transactions on Cybernetics 2015. His current research interests include fuzzy control and fuzzy systems, network control, multiagent systems, stochastic control

Hongjing Liang (Member, IEEE) received the B.S. degree in mathematics from Bohai University in 2009, and the M.S. degree in fundamental mathematics and the Ph.D. degree in control theory and control engineering in 2016, both from Northeastern University. He was a Temporary Research Associate with the Science Program, Texas A&M University, USA. He is currently a Professor with the University of Electronic Science and Technology of China. His current research interests include adaptive control, fuzzy control, and cooperative control and their applications. Dr. Liang was the recipient of the Outstanding Reviewer Award of IEEE/CAA Journal of Automatica Sinica 2019. He has been in the Editorial Board of International Journal of Fuzzy Systems, Fluctuation and Noise Letters
Corresponding author: Huaguang Zhang, e-mail: hgzhang@ieee.org
Received Date: 2021-08-04
Revised Date: 2021-11-02
Accepted Date: 2022-01-24

Available Online: 2022-03-19

Abstract

Abstract

This paper investigates adaptive containment control for a class of fractional-order multi-agent systems (FOMASs) with time-varying parameters and disturbances. By using the bounded estimation method, the difficulty generated by the time-varying parameters and disturbances is overcome. The command filter is introduced to solve the complexity problem inherent in adaptive backstepping control. Meanwhile, in order to eliminate the effect of filter errors, a novel distributed error compensating scheme is constructed, in which only the local information from the neighbor agents is utilized. Then, a distributed adaptive containment control scheme for FOMASs is developed based on backstepping to guarantee that the outputs of all the followers are steered to the convex hull spanned by the leaders. Based on the extension of Barbalat’s lemma to fractional-order integrals, it can be proven that the containment errors and the compensating signals have asymptotic convergence. Finally, three simulation examples are given to show the feasibility and effectiveness of the proposed control method.
- Adaptive backstepping control,
- command filter,
- fractional-order multi-agent system,
- time-varying parameters

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References(48)

References

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Explore adaptive containment control for the nonlinear FOMASs with unknown time-varying parameters and disturbances
A novel distributed error compensating scheme is constructed to counteract the effect of the filter errors
The bounded estimation approach is designed to overcome the difficulty generated by time-varying parameters and disturbances in the fractional-order nonlinear system

Adaptive Containment Control for Fractional-Order Nonlinear Multi-Agent Systems With Time-Varying Parameters

doi: 10.1109/JAS.2022.105545

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通讯作者: 陈斌, bchen63@163.com

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