REAL-TIME DECENTRALIZED NEURAL BLOCK CONTROL: APPLICATION TO A TWO DOF ROBOT MANIPULATOR

Abstract

This paper presents a discrete-time decentralized control scheme for trajec- tory tracking of a two degrees of freedom (DOF) robot manipulator. A modied recur- rent 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 techniques. The neural network learning is performed on-line by extended Kalman ltering (EKF). The controllers are designed for each joint using only local angular position and velocity measurements, simplifying computation com- plexity. The proposed scheme is implemented in real-time to control a two DOF robot manipulator.