Event-triggered adaptive decentralised control for switched interconnected nonlinear systems with unmodeled dynamics and full state constraints

Abstract

In this paper, an event-triggered adaptive decentralised control strategy for a class of switched interconnected nonlinear systems is presented, which considers full-state constraints and unmodeled dynamics, simultaneously. In the controller design process, the approximation capability of radical basis function neural networks (RBF NNs) is used to estimate the unknown functions of the system. The interference caused by unmodeled dynamics is overcome by introducing a dynamic signal. In addition, the barrier Lyapunov function (BLF) is constructed for each subsystem to dispose the influence of state constraints. An adaptive control scheme with event-triggered mechanism is proposed to reduce communication burden. It is shown that the proposed event-triggered controller and an adaptive neural decentralised control strategy are designed such that all the signals in the closed-loop system are guaranteed to be bounded, the tracking errors of the system converge to a small neighbourhood of the origin and the full state constraints are not violated. Finally, a simulation result shows the effectiveness of the developed approach.

Keywords:

• Adaptive control
• event-triggered control
• full state constraints
• switched interconnected nonlinear systems
• unmodeled dynamics

Disclosure statement

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

Data availability statement

The data used to support the findings of this study are included within the article.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China [grant numbers 61873151, 61803237, and 61703231]; and in part by the Shandong Provincial Natural Science Foundation, China [grant number ZR2019MF009]; and the Taishan Scholar Project of Shandong Province of China [grant number tsqn201909078].

Notes on contributors

Yutong Yin

Yutong Yin received the B.Sc. degree in Information and computing science from Bohai University, Jinzhou, China, in 2019, where she is currently pursuing the M.Sc. degree with the School of Mathematical Sciences. Her current research interests include adaptive neural control, and nonlinear systems.

Ben Niu

Ben Niu received the Ph.D. degree in control theory and applications from Northeastern University in 2013. He is currently a Professor at the College of Information Science and Engineering, Shandong Normal University. His research interests are switched systems, stochastic systems, robust control, intelligent control and their applications.

Kun Jiang

Kun Jiang was born in Linyi, Shandong, China, in 1993. He received the B.S. degree in computer science and technology from the Qingdao University of Technology, Qingdao, China, in 2017. He is currently pursuing the M.S. degree in computer software and theory from Shandong Normal University, Jinan, China. His current research interests include nonlinear systems, neural networks, and adaptive control.

Hao Jiang

Hao Jiang received the B.Sc. degree in Information and computing science from Huaiyin Institute of Technology, Huaian, China, in 2020, where he is currently pursuing the M.Sc. degree with the Bohai University of Mathematical Sciences. He current research interests include adaptive neural control, and multiagent systems.

Huanqing Wang

Huanqing Wang received the B.Sc. degree in mathematics from Bohai University in 2003, the M.Sc. degree in mathematics from Inner Mongolia University in 2006, and the Ph.D. degree from the Institute of Complexity Science in 2013. He was a Post-Doctoral Fellow with the Department of Electrical Engineering, Lakehead University, Canada, in 2014, and was a Post-Doctoral Fellow with the Department of Systems and Computer Engineering, Carleton University, 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.