Neural network-based adaptive asymptotic tracking of nonstrict feedback nonlinear systems with state constraints

Abstract

This paper devotes to develop an adaptive neural network (ANN) asymptotic tracking control strategy for nonstrict feedback nonlinear systems subject to state constraints. With the aid of barrier Lyapunov function, the state constraints are ingeniously addressed. By combining a bound estimation scheme with adaptive backstepping technique, an ANN asymptotic controller is recursively constructed. In addition, by selecting the appropriate Lyapunov function, the asymptotic convergence feature is achieved and the predefined state constraints are not transgressed. Finally, the validity of the presented control scheme is elucidated by numerical as well as practical examples.

Keywords:

• Asymptotic tracking control
• neural network
• backstepping technique
• state constraints
• nonlinear systems

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by the National Natural Science Foundation of China under grant numbers 61803116, 62173103, 52171299 and 52171302.