Neural Network-Based Adaptive Tracking Control for a Class of Nonlinear Systems
基于神经网络的一类非线性系统自适应跟踪控制

A neural network based adaptive tracking control scheme is proposed for a class of nonlinear systems. Two RBF neural networks are used to approximate the unknown nonlinear system, and a sliding model control term is used to eliminate the effects of the network inherent approximation errors and external disturbance. This control scheme can ensure the global stability of closed loop system and the asymptotical convergence of output tracking error.