Adaptive Neural Dynamic Surface Control for a General Class of Stochastic Nonlinear Systems with Time Delays and Input Dead-zone

This paper investigates adaptive tracking control for a more general class of stochastic nonlinear time-delay systems with unknown input dead-zone. For the considered system, the drift and diffusion terms contain time-delay state variables. In control design, Lyapunov-Krasovskii functionals are employed to handle unknown time-delay terms. Then, unknown nonlinear functions are approximated by RBF neural networks, and the dynamic surface control (DSC) technique is utilized to avoid the problem of explosion of complexity. At last, based on the Lyapunov stability theory, a robust adaptive controller is designed to guarantee that all closed-loop signals are bounded in probability and the tracking error converges to a small neighborhood of the origin. The simulation example is presented to further show the effectiveness of the proposed approach.