Iterative Learning Control for Multiple Point-to-Point Tracking Application

This paper considers a general class of linear iterative learning control (ILC) algorithm applied to tracking tasks which require the plant output to reach given points at predetermined time instants, without the specification of intervening reference points. A framework is developed in the frequency-domain in which the reference is updated between trials. It is shown that superior convergence and robustness properties are obtained compared with those associated with using the original class of ILC algorithm to track a prescribed arbitrary reference trajectory satisfying the point-to-point output constraints. Experimental results using a non-minimum phase test facility are presented to illustrate the theoretical findings.