Application of neural networks to decentralized control of robot manipulators with high degree of freedom

Abstract

In this paper, a neural network decentralized control for trajectory tracking of robot manipulators is developed. The proposed decentralized control allows the overall closed-loop system to be stabilized while making the tracking error to be uniformly ultimately bounded (UUB), without having any prior knowledge of the robot manipulator dynamics. The interconnections in the dynamic equations of each subsystem are considered with unknown nonlinear bounds. The RBF neural networks (RBFNNs) are proposed to model the unknown nonlinear dynamics of the robots and the interconnection terms. Using Lyapunov method, the stability of the overall system is investigated