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

Abstract

This paper presents a discrete-time decentralized control scheme for tra- jectory tracking of a two degrees of freedom (DOF) robot manipulator. A high order neural network (HONN) is used to approximate a decentralized control law designed by the backstepping technique as applied to a block strict feedback form (BSFF). The neural network learning is performed on-line by Kalman ltering. The controllers are designed for each joint using only local angular position and velocity measurements. These simple local joint controllers allow trajectory tracking with reduced computations. The proposed scheme is implemented in real-time to control a two DOF robot manipulator. Keywords: Decentralized control, High-order neural networks, Extended Kalman lter, Backstepping