Neural network-based asymptotic tracking control design for stochastic nonlinear systems

ABSTRACT

This article is focused on the adaptive neural network (ANN) asymptotic tracking control design for stochastic nonlinear systems with state constraints. The neural networks are utilised to deal with unknown uncertainties. The existence of state constraints and unknown virtual control coefficients (UVCC) bring many difficulties for control synthesis and analysis. With the aid of barrier Lyapunov function, the predefined state constraints are guaranteed. By fusing the lower bounds of UVCC into Lyapunov function construction, a novel ANN asymptotic tracking control method is devised by employing the bound estimation approach and backstepping technique. The presented asymptotic tracking controller can guarantee that the tracking error converges to zero in probability and the state constraints are not violated. The validity of the developed scheme is elucidated by simulation example.

Keywords:

• Asymptotic tracking control
• state constraints
• bound estimation approach
• stochastic nonlinear systems

Disclosure statement

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

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 in control 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.