Norm optimal iterative learning control based on a multiple model switched adaptive framework

In this paper a prominent class of iterative learning control (ILC) algorithm is reformulated in the framework of estimation-based multiple model switched adaptive control (EMMSAC). The resulting control scheme uses a bank of Kalman filters to assess the performance of a set of candidate plant models, and the ILC update at the end of each trial is constructed using the plant model with smallest residual. The underlying EMMSAC framework provides rigorous bounds for robust performance for unstructured uncertainties and without placing constraints on the underlying controllers. This paper hence addresses current limitations in ILC approaches for uncertain systems with experimental results from a highly relevant application of ILC in stroke rehabilitation confirming efficacy and scope.