Real-Time Decentralized Neural Control for a Five Dof Redundant Robot

Abstract

This paper presents a discrete-time decentralized control scheme for trajectory tracking of a five degrees of freedom (DOF) redundant robot. A modified recurrent high order neural network (RHONN) structure is used to identify the plant model and based on this model, a discrete-time control law is derived, which combines block control and sliding mode technique. The neural network learning is performed on-line by Kalman filtering. The local controllers for each joint use only local angular position and velocity measurements. The proposed control scheme is implemented in real-time. The experimental results for trajectory tracking show the effectiveness of the proposed approach.