Adaptive finite-time control for nonlinear multi-agent high-order systems with actuator faults

Abstract

This paper addresses the adaptive finite-time tracking control problem for a class of multi-agent high-order systems with actuator faults. By adding a power integrator and taking advantage of the approximation capabilities of radial basis function in neural networks, an adaptive backstepping controller is developed to overcome difficulties associated with the positive odd integer terms of nonlinear multi-agent high-order systems. Moreover, fault-tolerant control is used to tackle the impact of actuator failures. The developed adaptive finite-time control scheme ensures that the outputs of all followers track synchronously the reference signal quickly in finite time, and all signals of the controlled system are semi-globally uniformly finite-time stable. Simulation results demonstrate the feasibility of the proposed scheme.

Keywords:

• Adaptive finite-time control
• multi-agent systems
• high-order system
• actuator faults

Disclosure statement

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

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Additional information

Notes on contributors

Wen Bai

Wen Bai received the M.S. degree in Bohai University, Jinzhou, China, in 2020. He is currently pursuing for the Ph.D. degree in Beijing Jiaotong University, Beijing, China. Her current research interests include adaptive fault-tolerant control, adaptive fuzzy control, and nonlinear systems.

Peter Xiaoping Liu

Peter Xiaoping Liu (SM07) received his B.Sc. and M.Sc. degrees from Northern Jiaotong University, China in 1992 and 1995, respectively, and Ph.D. degree from the University of Alberta, Canada in 2002. He has been with the Department of Systems and Computer Engineering, Carleton University, Canada since July 2002 and he is currently a Professor. Dr. Liu has published more than 300 research articles. His interest includes interactive networked systems and teleoperation, haptics, surgical simulation, robotics, intelligent systems, and context-aware systems. Dr. Liu serves as an Associate Editor for several journals including IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Cybernetics, IEEE Transactions on Automation Science and Engineering, and IEEE Access. He is a licensed member of the Professional Engineers of Ontario (P.Eng), a senior member of IEEE and a Fellow of Engineering Institute of Canada (FEIC).

Huanqing Wang

Huanqing Wang received his B.Sc. degree in mathematics from Bohai University, Jinzhou, China, in 2003, his M.Sc. degree in mathematics from Inner Mongolia University, Huhhot, China, in 2006, and his Ph.D. degree from the Institute of Complexity Science, Qingdao University, Qingdao, China, in 2013. He was a Post-Doctoral Fellow with the Department of Electrical Engineering, Lakehead University, Thunder Bay, ON Canada, in 2014, and was a Post-Doctoral Fellow with the Department of Systems and Computer Engineering, Carleton University, Ottawa, ON Canada. He has authored or co-authored over 40 papers in top international journals. His current research interests include adaptive back-stepping control, fuzzy control, neural networks control, stochastic nonlinear systems. Dr. Wang serves as an Associate Editor for several journals, including Neural Computing and Applications, the International Journal of Control, Automation, and Systems, and the IEEE ACCESS.

Ming Chen

Ming Chen received the B.Sc. degree in automation from the Anshan Iron and Steel Institute, Anshan, China, in 2000, the M.Sc. degree in control theory and control engineering from the University of Science and Technology Liaoning, Anshan, in 2004, and the Ph.D. degree in control theory and control engineering from the University of Science and Technology Beijing, Beijing, China, in 2009. She is currently an Associate Professor with the School of Electronic and Information Engineering, University of Science and Technology Liaoning. Her research interests include nonlinear control systems, robust control, and fault-tolerant control.