Neural network-based adaptive tracking control of mobile robots in the presence of modelling error and disturbances

Abstract

This paper focuses on the tracking control of nonholonomic mobile robots. A kinematic control law and a dynamic control law are presented using backstepping theory. Furthermore, neural network control law is used to compensate for modelling error and approximate external disturbances in order to achieve the desired tracking performance. The global uniformly asymptotic stability of the system is guaranteed by Lyapunov theory. Simulation results are displayed to demonstrate the performance of the adaptive control law.