RBF NN-Based Backstepping Adaptive Control for a Class of Nonlinear Systems

Abstract

There are many control methods for nonlinear systems, but some of them can not control nonlinear mismatched systems very well. Backstepping control has obvious advantages in controlling nonlinear mismatched systems. Thus we proposed a new radial-basis-function (RBF) neural network-based backstepping adaptive controller combining RBF neural network (RBF NN) and backstepping control for a class of nonlinear mismatched systems. We adopted RBF NN to approximate the system uncertainty. And we analyzed the controller stability using Lyapunov stability theory. Finally we chose sine signal as simulation input signal, simulation results show that the proposed control strategy has better adaptive ability and robustness than PID control, validating the effectivess of the proposed control strategy.