Adaptive Neural Control of Stochastic Nonlinear Time-Delay Systems With Multiple Constraints
针对一类带有预定跟踪约束、输入饱和与输出死区的随机非线性时滞系统，提出一种自适应神经控制方法，利用反步法和神经网络逼近实现跟踪控制，并通过仿真验证了有效性。
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.