Disturbance observer-based adaptive neural network FTC for a class of nonlinear MASs with an estimated efficiency factor

ABSTRACT

An adaptive neural network fault-tolerant control(FTC) scheme is proposed for nonlinear and nonstrict-feedback multi-agent systems (MASs) with directed fixed topology. Firstly, a disturbance observer is designed to estimate the unknown external disturbances in the systems, and realise the dynamic estimation of the disturbances. Secondly, the efficiency factor is estimated online, and then the FTC scheme is designed successfully under the backstepping framework. It is proved that all signals in the closed-loop systems are semi-globally uniformly bounded and the tracking error is controlled in a small range. Finally, an example is given to verify the effectiveness of the proposed method.

KEYWORDS:

• Adaptive control
• disturbance observer
• fault-tolerant control
• Lyapunov function
• neural network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China [grant numbers 62025303, 62173173, and 61903168], and in part by the Liaoning Revitalization Talents Program [grant number XLYC2007177].

Notes on contributors

Jiyang Jia

Jiyang Jia received her B.S. degree in information and computational science and M.S. degree in operations research and cybernetics from the Liaoning University of Technology, Jinzhou, China, in 2019 and 2022, respectively. Her main research direction is intelligent control theory and application. The main research contents include nonlinear multi-agent system, neural network control, fault-tolerant control, constraint control and so on.

Jie Lan

Jie Lan received the B.S. degree in Applied Mathematics and the M.S. degree in Control Theory and Control Engineering from Jilin Agricultural University, Changchun, China, in 2005 and Liaoning University of Technology, Jinzhou, China, in 2011, respectively. She is a doctoral student in Agricultural Electrification and Automation from Shenyang Agricultural University, Shenyang, China, in 2019. She is currently a lecturer with the College of Science, Liaoning University of Technology. She is now a member of Chinese association of automation. Her research interests include adaptive fuzzy control, nonlinear control, neural network control, multi-agent and swarm intelligence.

Yan-Jun Liu

Yan-Jun Liu received the B.S. degree in applied mathematics and the M.S. degree in control theory and control engineering from the Shenyang University of Technology, Shenyang, China, in 2001 and 2004, respectively, and the Ph.D. degree in control theory and control engineering from the Dalian University of Technology, Dalian, China, in 2007. He is currently a Professor with the College of Science, Liaoning University of Technology, Jinzhou, China. His current research interests include intelligent control and swarm intelligence control. Dr. Liu is currently an Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics and IEEE/CAA Journal of Automatica Sinica.

Lei Liu

Lei Liu received the B.S. degree in information and computing science and the M.S. degree in applied mathematics from the Liaoning University of Technology, Jinzhou, China, in 2010 and 2013, respectively. He received the Ph.D. degree in 2017 from the Northeastern University, Shenyang, China. Currently, he is a lecturer at the Liaoning University of Technology, Jinzhou, China. His current research interests include fault-tolerant control, fault detection and diagnosis, optimal control for nonlinear systems, neural network control and their industrial applications.