Real-time five DOF robot control using a decentralized neural backstepping scheme

This paper presents a discrete-time decentralized control scheme for trajectory tracking of a five degrees of freedom (DOF) redundant robot. 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 filtering. 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 applicability of the proposed scheme is illustrated via real-time implementation.