Multiple Model Iterative Learning Control for FES-based Stroke Rehabilitation

Abstract

Functional electrical stimulation (FES) is an effective upper limb stroke rehabilitation technology that helps patients recover lost movement by assisting functional task training. Unfortunately, current FES controllers cannot satisfy the competing demands of high accuracy, robustness to modelling error and limited set-up/identification time needed for clinical or home deployment. To address this, an estimation-based multiple model switched iterative learning control framework is proposed, combining the most successful adaptive learning features of existing FES controllers. A practical design procedure that guarantees robust performance is developed, and efficacy is established across realistic testing scenarios.