Robust tracking control for a class of uncertain electrically driven robots

The problem of designing robust tracking controls for a large class of robots actuated by brushed direct current motors is addressed. This class of electrically driven robots can be perturbed by plant uncertainties, unmodelled time-varying perturbations and external disturbances. Adaptive neural network (or fuzzy) systems are employed to approximate the behaviours of uncertain dynamics, and the variable structure system control algorithm is used to efficiently eliminate the effect of both the approximation error and the time-varying perturbation. Consequently, a hybrid robust adaptive dynamic feedback tracking controller is developed such that all the states and signals of the closed-loop system are bounded and the asymptotic bound on the trajectory tracking error can be made arbitrarily small. Finally, simulation examples are presented to demonstrate the effectiveness and the tracking performance of the proposed control algorithm.