Learning control applied to feedforward compensator tuning

Abstract

When a robot performs the same task repeatedly, a learning controller can enhance the performance of the system significantly. The function of the learning controller is to update the feedforward compensator which cancels the dynamics of robot manipulators and friction of each joints. The feedforward compensator especially plays an important role in the contact force control of robot manipulators with a rigid workpiece. In this paper, the learning algorithm which guarantees the asymptotic stability is studied and used to refine the feedforward compensator. Experiments with a two degree of freedom direct drive robot confirms the effectiveness of the algorithm.<>