On-line learning control of manipulators based on artificial neural network models

Abstract

This paper addresses the tracking control problem of robotic manipulators with unknown and changing dynamics. In this study, nonlinear dynamics of the robotic manipulator is assumed to be unknown and a control scheme is developed to adaptively estimate the unknown manipulator dynamics utilizing generic artificial neural network models to approximate the underlying dynamics. Based on the error dynamics of the controller, a parameter update equation is derived for the adaptive ANN models and local stability properties of the controller are discussed. The proposed scheme is simulated and successfully tested for trajectory following tasks. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics.