Adaptive Fixed-Time Control of Nonlinear MASs With Actuator Faults

Hongru Ren; Hui Ma; Hongyi Li; Zhenyou Wang

doi:10.1109/JAS.2023.123558

Volume 10 Issue 5

May 2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2023 > 10(5): 1252-1262

H. R. Ren, H. Ma, H. Y. Li, and Z. Y. Wang, “Adaptive fixed-time control of nonlinear MASs with actuator faults,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1252–1262, May 2023. doi: 10.1109/JAS.2023.123558

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H. R. Ren, H. Ma, H. Y. Li, and Z. Y. Wang, “Adaptive fixed-time control of nonlinear MASs with actuator faults,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1252–1262, May 2023. doi: 10.1109/JAS.2023.123558

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Adaptive Fixed-Time Control of Nonlinear MASs With Actuator Faults

doi: 10.1109/JAS.2023.123558

Hongru Ren^{1
,
,},
Hui Ma^{2
,
,
,},
Hongyi Li^{1
,
,},
Zhenyou Wang^{2
,
,}

1.
School of Automation and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control, Guangdong University of Technology, Guangzhou 510006, China
2.
School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou 510006, China

Funds: This work was supported in part by the National Natural Science Foundation of China (62003093, 62203119, 62033003, 62121004), the China National Postdoctoral Program (BX20220095, 2022M710826), the Natural Science Foundation of Guangdong Province (2022A1515011506), and the Guangzhou Science and Technology Planning Project (202102020586)

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Author Bio:
Hongru Ren (Member, IEEE) received the B.E. and Ph.D. degrees in control science and engineering from the University of Science and Technology of China, in 2013 and 2019, respectively. He is with the School of Automation and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control, Guangdong University of Technology. His research interests include complex nonlinear systems, networked control systems and unmanned autonomous systems

Hui Ma received the B.S. degree in mathematics from Harbin Normal University in 2016, the M.S. degree in applied mathematics from Bohai University in 2019, and the Ph.D. degree in control science and engineering from the Guangdong University of Technology in 2021. She is currently a Postdoctoral Researcher with the School of Mathematics and Statistics, Guangdong University of Technology, and was awarded the fellowship of China National Postdoctoral Program for Innovative Talents. Her research interests include adaptive neural network control and adaptive fuzzy control

Hongyi Li (Senior Member, IEEE) received the Ph.D. degree in intelligent control from the University of Portsmouth, UK in 2012. He was a Research Associate with the Department of Mechanical Engineering, University of Hong Kong, and Hong Kong Polytechnic University, China. He was a Visiting Principal Fellow with the Faculty of Engineering and Information Sciences, University of Wollongong, Australia. He is currently a Professor with the Guangdong University of Technology. His research interests include intelligent control, cooperative control, sliding mode control and their applications. Prof. Li was a recipient of the 2016 and 2019 Andrew P. Sage Best Transactions Paper Awards from IEEE System, Man, Cybernetics Society, the Best Paper Award in Theory from ICCSS 2017, and the Zadeh Best Student Paper from IEEE ICCSS 2019. He has been on the editorial board of several international journals, including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Systems, Man and Cybernetics: Systems, IEEE Transactions on Cognitive and Developmental Systems, Science China Information Sciences, IEEE/CAA Journal of Automatica Sinica, Journal of Systems Science and Complexity, Neural Networks, Asian Journal of Control, Circuits, Systems and Signal Processing, and International Journal of Control, Automation and Systems. He has been the Guest Editor of IEEE Transactions on Cybernetics and IET Control Theory and Applications. He is a Member of the IFAC Technical Committee on Computational Intelligence in Control

Zhenyou Wang received the B.S. degree in mathematics from Ludong University in 2000, the M.E. degree in mechanics from South China University of Technology in 2003, and the Ph.D. degree in mathematics from the Sun Yat-sen University in 2015. He is with the School of Mathematics and Statistics, Guangdong University of Technology. His research interests include data analysis and optimization
Corresponding author: Hui Ma, e-mail: huima2022@gdut.edu.cn
Received Date: 2023-02-07
Accepted Date: 2023-03-10

Available Online: 2023-03-27

Abstract

Abstract

The adaptive fixed-time consensus problem for a class of nonlinear multi-agent systems (MASs) with actuator faults is considered in this paper. To approximate the unknown nonlinear functions in MASs, radial basis function neural networks are used. In addition, the first order sliding mode differentiator is utilized to solve the “explosion of complexity” problem, and a filter error compensation method is proposed to ensure the convergence of filter error in fixed time. With the help of the Nussbaum function, the actuator failure compensation mechanism is constructed. By designing the adaptive fixed-time controller, all signals in MASs are bounded, and the consensus errors between the leader and all followers converge to a small area of origin. Finally, the effectiveness of the proposed control method is verified by simulation examples.
- Actuator faults,
- adaptive fixed-time control,
- multi-agent systems (MASs),
- Nussbaum function

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References(49)

References

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The adaptive fixed-time consensus problem for a class of nonlinear multi-agent systems (MASs) with actuator faults is considered in this paper. By designing the adaptive fixed-time controller, all signals in MASs are bounded, and the consensus errors between the leader and all followers converge to a small area of origin
The adaptive fixed-time stability criterion is extended to a more general class of nonlinear MASs with actuator faults. Compared with existing work results, this paper proposes a more helpful tool for solving the problem of approximation-based adaptive fixed-time control for nonlinear uncertain systems
A dynamic surface control method employing the sliding mode differentiator is used in this paper, it can avoid the tedious analytic computation and “explosion of complexity” problem simultaneously in the conventional backstepping method. Different from the filtering methods in existing work results, a filtering error compensation mechanism is proposed in this paper. Compared with the above compensation mechanisms, it not only makes the filtering errors bounded in fixed time, but also easier to design
In addition, an actuator failure model with gain fault, bias fault and unknown control direction is constructed, and the Nussbaum function is used to build the actuator fault compensation mechanism. The proposed control strategy ensures that all error signals in MASs are bounded and the consensus is achieved in fixed time

Adaptive Fixed-Time Control of Nonlinear MASs With Actuator Faults

doi: 10.1109/JAS.2023.123558

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