Design of a robust neural network-based tracking controller for a class of electrically driven nonholonomic mechanical systems

This paper addresses the problem of designing robust tracking controls for a class of uncertain nonholonomic systems actuated by brushed direct current (DC) motors. This class of electrically driven nonholonomic mechanical systems can be perturbed by plant uncertainties, unmodeled time-varying perturbations, and external disturbances. An adaptive neural network-based dynamic feedback tracking controller will be developed such that all the states and signals of the closed-loop system are bounded and the trajectory tracking error is as small as possible. Consequently, for practical applications, the intelligent robust tracking control scheme developed here can be employed to handle a broader class of electrically driven nonholonomic systems in the presence of high-degree time-varying uncertainties. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed control algorithms.