A Neural Network Based Adaptive Tracking Controller for Nonholonomic Wheeled Mobile Robots With Unknown Dynamics

This paper presents the design, implementation and comparative analysis of an intelligent neural network based controller used for adaptive trajectory tracking of a wheeled mobile robot with unknown dynamics. In this proposed control scheme, the neural network is used to continuously tune the gains of the kinematic based controller in a backstepping structure. The online learning and adaptive capabilities of the neural network are utilized to achieve a smooth and fast robot tracking motion. The simulation results are used to verify the tracking performance of the proposed control algorithm and to compare it with the conventional backstepping controller.