Neural networks-based adaptive tracking control for full-state constrained switched nonlinear systems with periodic disturbances and actuator saturation

Abstract

In this paper, an adaptive tracking control approach is developed for full-state constrained switched nonlinear systems that have actuator saturation, periodic disturbances and unknown control direction. To deal with the full-state constraints, the Barrier Lyapunov functions are introduced to limit the state variables within the corresponding constraint conditions. Meanwhile, the Fourier series expansion technology is employed to deal with unknown periodic disturbances and unknown nonlinear dynamics jointly. Additionally, a Nussbaum-type function is used in the controller design to cope with the and unknown control gain and input saturation. On the basis of the Lyapunov stability theory, it is demonstrated rigorously that all signals of the closed-loop system are uniformly ultimately bounded, and the proposed controller ensures that the tracking error is kept within a compact set close to zero. In the end, the validity of the designed control protocol is verified by a simulation example.

Keywords:

• Switched nonlinear systems
• barrier Lyapunov functions
• Fourier series expansion technology
• periodic disturbance
• Nussbaum-type function

Acknowledgement

This research work was funded by Institutional Fund Projects under grant no. (IFPIP: 134-611-1443). The authors gratefully acknowledge technical and financial support provided by the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.

Disclosure statement

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

Data availability statement

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

Additional information

Notes on contributors

Yumeng Cao

Yumeng Cao was born in Shandong Province, China, in 1998. She received the B.S. degree in electrical engineering and automation from Ludong University, Yantai, China, in 2020. She is currently working toward the M.S. degree in control science and engineering. Her research interests include event-triggered control and nonlinear systems.

Ning Xu

Ning Xu received the B.S. degree from Harbin Engineering University, Harbin, China, in 2005, and the M.S. degree from the Harbin Institute of Technology, Harbin, in 2012. She is currently pursuing the Ph.D. degree with the Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, China. Since 2013, she has been with the College of Information Science and Technology, Bohai University, Jinzhou, China, where she is currently a Lecturer. Her research interests include switched systems, nonlinear systems, and sampled-data systems.

Huanqing Wang

Huanqing Wang received the B.Sc. degree in mathematics from Bohai University, Jinzhou, China, in 2003, the M.Sc. degree in mathematics from Inner Mongolia University, Hohhot, China, in 2006, and the Ph.D. degree from the Institute of Complexity Science, Qingdao University, Qingdao, China, in 2013.He is currently a Lecturer with the College of Mathematics and Physics, Bohai University. His current research interests include nonlinear control, adaptive fuzzy control, and stochastic nonlinear systems.

Xudong Zhao

Xudong Zhao received the B.S. degree in Automation from Harbin Institute of Technology in 2005 and the Ph.D. degree from Control Science and Engineering from Space Control and Inertial Technology Center, Harbin Institute of Technology in 2010. Dr. Zhao was a lecturer and an associate professor at the China University of Petroleum, China. From March 2013, he was with Bohai University, China, as a professor. In 2014, Dr. Zhao worked as a postdoctoral fellow in the Department of Mechanical Engineering, the University of Hong Kong. Since December 2015, he has been with Dalian University of Technology, China, where he is currently a Professor. He is an Associate Editor of the IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS, Non linear Analysis: Hybrid Systems, Neurocomputing, International Journal of General Systems, ACTA Automatica Sinica, Assembly Automation, and Journal of Aeronautics.

Adil M. Ahmad

Adil M. Ahmad received the B.S. and M.Sc. degrees in computer science from King Abdulaziz University, Jeddah, Saudi Arabia, and Rochester Institute of Technology, Rochester, New York, USA, respectively. He then received the Ph.D. degree in security from the University of Colorado Springs, Colorado Springs, Colorado, USA. He is currently an assistant professor of the Information Technology Department within the Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia. He has research interests in adaptive control, machine learning, deep learning, Mobile Computing and security.