Force and position control of robot manipulator using neurocontroller with GA based training

In this paper we propose a force and position controller for a robot manipulator using a neurocontroller (NC) with genetic algorithm (GA) based training. It is very difficult to design the controller which applies both force and position control to the robot manipulator. We use a simple three layered neural network as the controller, and the training method of the NC is GA based. Inputs to the NC are errors of the position and force. Furthermore, we input the integral information of the position error to the NC because it eliminates the steady-state position error. Simulation shows that the proposed NC has better performance for both position and force control than the conventional neural network, for the robot manipulator.