Learning accurate path control of industrial robots with joint elasticity

Abstract

An adaptive architecture for feedforward control of industrial robots with standard positional controller is presented. This approach explicitly considers robots with elastic joints. It assumes that the real position of the tool centre point (TCP) can be recorded for offline evaluation. Compensation of elasticity is trained in a feedforward controller. Adaptation takes place without any knowledge of the physical system model. The performance of the method is demonstrated in experiments with a 6-axis industrial robot KUKA KR6/1 for which real path errors during full speed motion are reduced by 70%. Reductions of 50% can be expected for untrained paths or other robots of the same type.