On the use of learning control for improved performance in robot control systems

Abstract

Iterative learning control applied to a simplified model of a robot arm is studied. The iterative learning control input signal is used in combination with conventional feed-back and feed-forward control, and the aim is to let the learning control signal handle the effects of unmodeled dynamics and friction. Convergence and robustness aspects of the choice of filters in the updating scheme of the iterative learning control signal are studied.