Adaptive Neural Network Sliding Mode Trajectory Tracking Control for Non-holonomic Wheeled Mobile Robots

Abstract

An adaptive neural sliding mode control strategy with the self-tuning of robust item coefficients is proposed for the trajectory tracking of non-holonomic wheeled mobile robots.Firstly,a kinematic controller is designed by means of backstepping technique.Then,the dynamic controller is proposed based on sliding mode control method,in which the upper bound of the uncertainties is adaptively approximated by RBF neural networks and the robust item coefficients are self-tuned.Thus,the disadvantage of the traditional sliding mode controller,which needs to know the boundary of the system uncertainties in advance,is overcome.By using Lyapunov stability theorem,both the stability of closed-loop system and the asymptotical convergence of tracking errors are ensured.Simulation results further validate the effectiveness of the proposed controller.