Iterative learning control for multiple point-to-point tracking application

A framework is developed which enables a general class of linear Iterative Learning Control (ILC) algorithms to be applied to tracking tasks which require the plant output to reach given points at predetermined time instants, without the need for intervening reference points to be stipulated. 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 position constraints. Experimental results using a non-minimum phase test facility are presented to confirm the theoretical findings