Decentralized control of robotic manipulators with neural networks

Abstract

A decentralized neuro-controller with feedback error learning is proposed in this paper to deal with robot manipulator tracking problem. The PD + nonlinear (NL) feedback law + robustifying signal ensure global stability while the neural networks are utilized to compensate the decentralized nonlinear terms in the robot manipulator dynamics so that both robustness and good tracking performance are achieved. In addition to the theoretical proof of global stability, the effectiveness of the proposed scheme is also demonstrated by comparing the tracking performance of the neuro-controller for a two-link robot manipulator with that of the conventional decentralized adaptive controller.