Discrete-time decentralized neural block controller for a five DOF robot manipulator

This paper presents a discrete-time decentralized control scheme for identification and trajectory tracking of a five degrees of freedom (DOF) robot manipulator. A recurrent high order neural network (RHONN) structure is used to identify the robot model, and based on this model a discrete-time control law is derived, which combines discrete-time block control and sliding modes techniques. The neural network learning is performed online using Kalman filtering. A controller is 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 simulations.