Lifted system iterative learning control applied to an industrial robot

This paper proposes a model-based iterative learning control algorithm for time-varying systems with a high convergence speed. The convergence of components of the tracking error can be controlled individually with the algorithm. The convergence speed of each error component can be maximised unless robustness for noise or unmodelled dynamics is needed. The learning control algorithm is applied to the industrial Stäubli RX90 robot. A linear time-varying model of the robot dynamics is obtained by linearisation of the non-linear dynamic equations. Experiments show that the tracking error of the robot joints can be reduced to the desired level in a few iterations.