Non-linear multiple model switched iterative learning control

This paper develops a multiple model switched iterative learning control (ILC) framework for a general class of nonlinear plant dynamics. Given a set of candidate plant models chosen by the designer as possible representations of the true plant, the switching framework converges to a bounded tracking error whose norm is proportional to the (modified)gap metric between the true plant and the closest model in the candidate model set. This is the first multiple model switched ILC framework to provide guaranteed tracking performance for non-linear systems using a general class of ILC update(including Newton, norm-optimal, and gradient types) subject to unstructured model uncertainty. The transparent design framework is illustrated through application of the framework to a simulation example which demonstrates precise tracking error in the presence of arbitrarily large plant uncertainty.