Robust adaptive neural tracking control for a class of electrically driven robots with time delays

摘要

This article addresses the problem of designing the robust tracking control for a class of uncertain electrically driven robots with time delays. The unknown time-delay uncertainty is assumed to be bounded by a function of all the state variables. By suitably choosing the Lyapunov-Krasovskii functionals, a novel adaptive/robust neural tracking control scheme is developed for the first time such that all the states and signals of the closed-loop time-delay robot system are bounded and the tracking error is shown to be uniformly ultimately bounded. By suitably designing the embedded current signal, the effect of time-delay uncertainty in the mechanical dynamics does not require to be incorporated into the current tracking error dynamics, and so the Lyapunov-Krasovskii functionals can be easily constructed in the stability analysis. Compared with the previous investigations of controlling robots the control scheme developed here can be extended to handle a broader class of electrically driven robots perturbed simultaneously by plant uncertainties, time-varying perturbations, and time-delay uncertainties. Finally, simulation examples are made to demonstrate the effectiveness of the proposed control algorithm.