Adaptive fuzzy event-triggered control for nonstrict-feedback switched stochastic nonlinear systems with state constraints

Abstract

Due to the frequent occurrence of random disturbances in practical physical systems, the control design of stochastic systems has a significant application background. This article presents an event-triggered adaptive fuzzy control scheme for nonstrict-feedback switched stochastic nonlinear systems with state constraints. The barrier Lyapunov functions are deployed to make all states maintain the prescribed regions. In addition, the event-triggered mechanism is incorporated into the backstepping framework to mitigate the data transmission. The fuzzy logic systems are exploited to cope with the system uncertainties, and then the adaptive fuzzy control strategy is recursively constructed. The devised event-triggered adaptive fuzzy controller can not only surmount the influence of state constraints but also decrease unnecessary resource consumption. In virtue of common Lyapunov function method, it is shown that all system signals are bounded under switching signals and the predefined constraints are not violated. Finally, the validity of the presented scheme is elucidated by simulation results.

Keywords:

• Event-triggered control
• state constraints
• switching signals
• switched stochastic nonlinear systems

Acknowledgements

This work was supported in part by the Development Project of Ship Situational Intelligent Awareness System under Grant MC-201920-X01, in part by the National Natural Science Foundation of China under Grant 61673129.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported in part by the Development Project of Ship Situational Intelligent Awareness System under Grant MC-201920-X01, in part by the National Natural Science Foundation of China under Grant 61673129.

Notes on contributors

Yongchao Liu

Yongchao Liu received the M.S. degree in control science and engineering from Dalian Maritime University, Dalian, China, in 2017. He is currently pursuing the Ph.D. degree from the College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China. His research interests include nonlinear adaptive control, fuzzy control and neural network control for nonlinear systems.

Qidan Zhu

Qidan Zhu received the B.S. degree in automatic control and the M.S. and Ph.D. degrees incontrol theory and control engineering from Harbin Engineering University, Harbin, China, in 1985, 1987, and 2001, respectively. Now, he is currently a Professor with the College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China. His research interests include nonlinear control and intelligent technology and application for autonomous system and robotic.