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

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 .