Neuro-adaptive sliding-mode tracking control of robot manipulators

In this work, a new dynamical on-line learning algorithm for robust model-free neuro-adaptive control of a class of nonlinear systems with uncertain dynamics is proposed and experimentally tested in order to evaluate its performance and practical feasibility in industrial settings. The control application studied is the trajectory tracking control task for the first three joints of an open architecture articulated robot manipulator. The control scheme makes use of variable structure systems theory and the feedback-error-learning concept. An inner sliding motion is established in terms of the neurocontroller parameters, aiming to lead the error in its control signal towards zero. The outer sliding motion bears on the system under control, the state tracking error vector of which is simultaneously driven towards the origin of the phase space. The existing relation between the two sliding motions is shown. Experimental results illustrate that the proposed neural-network-based controller possesses a remarkable learning capability to control complex dynamical systems, virtually without requiring a priori knowledge of the plant dynamics and laborious start-up procedures. Copyright (c) 2007 John Wiley & Sons, Ltd.