Iterative Learning Control with variable pass length applied to trajectory tracking on a crane with output constraints

A typical application of Iterative Learning Control (ILC), namely trajectory tracking on a lab-scale gantry crane, is considered. However, the load is only allowed to move in the close proximity of the reference trajectory. Since these output constraints lead to disrupted trials, the pass length in this ILC system is not constant. In this contribution, we present new results on convergence and new combinations of methods for this class of ILC systems and apply them to the given application. Simulation and experimental results are provided which demonstrate that both maximum pass length and small tracking error can be achieved in very few iterations even in the presence of tight constraints and model uncertainties.