Neural network adaptive finite‐time control of stochastic nonlinear systems with full state constraints
Qidan Zhu, Yongchao Liu
Abstract
Abstract This paper investigates the issue of neural network adaptive finite‐time tracking control for stochastic nonlinear systems subject to full state constraints. In the controller design process, neural networks are employed to cope with the packed uncertainties and the log‐type barrier Lyapunov functions are introduced to prevent the violation of the state constraints. By using approximated‐based neural networks and adaptive backstepping technique, a novel finite‐time control approach is presented. The designed control method not only makes the tracking error converge to a small neighborhood of the origin in a finite time, but also surmounts the effect of state constraints to system performance. A numerical simulation example is provided to illustrate the validity of the designed control method.