Finite-time command filter-based adaptive fuzzy output-feedback control for MIMO nonlinear systems with multiple actuator constraints

Abstract

In this article, the finite-time adaptive fuzzy output-feedback control strategy is presented for a class of multi-input and multi-output (MIMO) nonlinear systems with multiple actuator constraints and unmeasured states. Fuzzy logic systems and a fuzzy adaptive state observer are adopted to estimate the nonlinear uncertainties and the unmeasured states, respectively. The inherent ‘explosion of complexity’ problem is eliminated by adopting command filter technology and the corresponding error compensation mechanism is exploited to alleviate the influence of the errors generated by command filters. It is further proved that all the signals can be fast finite-time bounded in the closed-loop systems based on the Lyapunov stability theory. Besides, the observer and tracking errors can reach a small region around the origin in fast finite time. Finally, the practicability of the presented theoretic result can be demonstrated through the numerical and practical examples.

Keywords:

• Fuzzy logic systems
• multiple actuator constraints
• unmeasurable state variables
• finite-time convergence
• command filter technique

Disclosure statement

No potential conflict of interest was reported by the authors

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62173046, Grant 51939001, and Grant 61976033; and in part by the Education Department of Liaoning Province through the General Project Research under Grant WZ2020002 and Grant LJ2020001.

Notes on contributors

Shijia Kang

Shijia Kang received the M.S. degree from Bohai University, Jinzhou, China, in 2020. She is currently pursuing the Ph.D. degree with Beijing Jiaotong University, Beijing, China. Her current research interests include adaptive backstepping control, command filter technique, intelligent control, and nonlinear systems.

Peter Xiaoping Liu

Peter Xiaoping Liu 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 backstepping 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.