Fast norm-optimal iterative learning control for industrial applications

Abstract

Norm-optimal iterative learning control has potential to significantly increase the accuracy of many trajectory tracking tasks which can be found in industry. The algorithm can achieve very low levels of tracking error and the number of iterations required to reach minimal error is small compared to many other iterative learning control algorithms. However, in the current format, the algorithm is not attractive to industry because it requires a large number of calculations to be performed at each sample instant. This implies that control hardware must be very fast which is expensive, or that the sample frequency must be reduced which can result in reduced performance. To remedy these problems, a revised version, fast norm-optimal iterative learning control is proposed which is significantly simpler and faster to implement. The new version is tested both in simulation and in practice on a three axis industrial gantry robot.