时变时滞随机非线性系统的自适应神经网络跟踪控制

Adaptive neural tracking control for stochastic nonlinear systems with time-varying delay

针对一类具有时变时滞的不确定随机非线性严格反馈系统的自适应跟踪问题,利用Razumikhin引理和backstepping方法,提出一种新的自适应神经网络跟踪控制器.该控制器可保证闭环系统的所有误差变量皆四阶矩半全局一致最终有界,并且跟踪误差可以稳定在原点附近的邻域内.仿真例子表明所提出控制方案的有效性.

This paper focuses on the adaptive neural control for a class of uncertain stochastic nonlinear strict-feedback systems with time-varying delay. Based on the Razumikhin function approach, a novel adaptive neural controller is de- veloped by using the backstepping technique. The proposed adaptive controller guarantees that all the error variables are 4-moment semi-globally uniformly ultimately bounded in a compact set while the tracking error remains in a neighborhood of the origin. The effectiveness of the proposed design is validated by simulation results.