MRC NN controller for arm robot manipulator

Abstract

This paper presents investigations into the development of model reference control based on a neural network (NN) for robot manipulator. A NN used as a controller network and a plant model network. A dynamic model of the system is derived using a Lagrange-Euler. The controller to simplify a nonlinearities problem that can be efficiently solved using NN. To study the effectiveness of the controller, initially a nonlinear model is developed for one link robot manipulator. The performances of the NN controllers are assessed in terms of the input tracking controller capability of the system and disturbance robustness. The input is generated by a combined multiple steps input. Finally, a comparative assessment of the input tracking control and a disturbance robustness is presented. The results show that NN controller performs give increasing profiles.