Adaptive Neural Control of Stochastic Nonlinear Time-Delay Systems With Multiple Constraints

For a class of stochastic nonlinear time-delay systems with multiple constraints-predefined tracking constraint, input saturation, and output dead zone-the output tracking control problem is addressed in this paper. By expressing the saturated actuator as a smooth nonlinear function and employing the Nussbaum function technique, the input and output constraints problems are solved. The tracking performance is achieved under the predefined tracking constraint by utilizing the backstepping recursive design technique and the approximation property of neural networks. Then, based on the utilization of the Lyapunov-Krasovskii functional, the stochastic stability of the closed-loop system is achieved. Finally, the proposed control method is verified through a simulation example.