Robust adaptive control for a class of nonlinear systems using backstepping based on RBF neural computing

A robust adaptive controller design method using backstepping techniques based on RBF neural computing for a class of uncertain nonlinear systems is proposed. RBF neural networks are used to approximate the uncertainties and to eliminate the bad effects of the uncertainties with robust terms in, the controller and virtual controllers. The adaptive tuning rules of matrixes arc derived by the Lyapunov stability theorem which guarantees all error signals of the closed-loop system are bounded and exponentially converge to a neighborhood of the origin. globally. The proposed method does not need the supper bounds of the uncertainties and their any order derivatives. In the end, simulation results are presented to demonstrate the effectiveness of the proposed method.