Event-triggered-based fixed-time adaptive prescribed tracking control for stochastic nonlinear systems with actuator faults

Abstract

In this paper, the event-triggered fixed-time control scheme is developed for a class of stochastic nonlinear systems with prescribed boundary constraints and actuator faults. For the controlled systems with unknown nonlinear functions, the neural networks are employed to reestablish the system model. Based on the event-triggered control technology, an original adaptive fixed-time control strategy with prescribed performance and the fault state is proposed by using the backstepping technique. Under the developed adaptive controller, the tracking error satisfies the predefined boundary functions and all the closed-loop system signals are bounded in probability in a fixed time, and the convergence time is irrelevant to the initial states of the system. The practicability of the designed controller is illustrated by a simulation example.

Keywords:

• Stochastic nonlinear systems
• adaptive neural network control
• fixed-time convergence
• event-triggered control
• fault-tolerant control

Disclosure Statement

No potential conflict of interest was reported by the authors.

7. Data availability statement

All data included in this study are available upon request by contact with the corresponding author.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [Grant numbers 62172135, 61603003].

Notes on contributors

Xu Zhang

Xu Zhang received her B.S. degree from Fuyang Normal University, China, in 2011 and M.S. degree from Hefei University of Technology, China, in 2014. She is currently a Doctoral Student of Hefei University of Technology. Her research interests are in stochastic systems and adaptive control.

Jieqing Tan

Jieqing Tan received the B.S. degree in Mathematics from Xi’an Jiaotong University in 1984, a Master’s degree in Mathematics from Hefei University of Technology and a Ph.D. in Computational Mathematics from Jilin University. He has worked in School of Mathematics of Hefei University of Technology since 1990 where he is now a professor. His current research interests include Non-linear Numerical Approximation, Computer Graphics, Image Processing and Intelligent Control.

Lei He

Lei He received the B.S. degree in Computer Science from Central China Normal University, a Master’s degree in Computer Software and Theory from Hefei University of Technology and a Ph.D in Computer Application Technology from Hefei University of Thechnology. She has worked in School of Mathematics of Hefei University of Technology since 2002 where she is now a associate professor. Her current research interests include Nonlinear Numerical Approximation and Video/Image Processing.

Yangang Yao

Yangang Yao received his B.S. degree from Fuyang Normal University, China, in 2012 and M.S. degree from Hefei University of Technology, China, in 2015. He is currently a Doctoral Student of Hefei University of Technology. His research interests are in the stability theory and adaptive control.

Jian Wu

Jian Wu received his Ph.D. degree in Applied Mathematics from Xidian University, Xi’an, China, in 2015. He is currently an Associate Professor with the School of Computer and Information, Anqing Normal University, Anqing, China. His current research interests include intelligent control, adaptive control, and adaptive switching control.