Decentralized Neural Block Control for a Robot Manipulator based in UKF training

Abstract

This work presents an alternative solution of the trajectory tracking problem for a two degrees of freedom (DOF) robot manipulator using a discrete-time decentralized control strategy. The local controller for each joint uses only local angular position and velocity measurements. A recurrent high order neural network (RHONN) structure is used to aproximate the manipulator dynamics, and based on this model, a discrete-time control law is computed, which combines block control and the sliding mode techniques. The neural network learning is performed on-line by Unscented Kalman filter (UKF) algorithm, a variation of nonlinear Kalman Filter.