Multiple-model iterative learning control with application to stroke rehabilitation

Abstract

Model-based iterative learning control (ILC) algorithms achieve high accuracy but often exhibit poor robustness to model uncertainty, causing divergence and long-term instability as the number of trials increases. To address this, an estimation-based multiple-model switched ILC (EMMILC) approach is developed based on novel theorem results which guarantee stability if the true plant lies within a uncertainty space defined by the designer. Using gap metric analysis, EMMILC eliminates restrictive assumptions on the uncertainty structure assumed in existing multiple-model ILC methods. Our design framework minimises computational load while maximising tracking accuracy. Applied to a common rehabilitation scenario, EMMILC outperforms the standard ILC approaches that have been previously employed in this setting. This is confirmed by experimental tests with four participants where performance increased by 28%. EMMILC is the first model-based ILC framework that can guarantee high performance while not requiring any model identification or tuning, and paves the way for effective, home-based rehabilitation systems.