Multiple Model Iterative Learning Control with Application to Upper Limb Stroke Rehabilitation

Abstract

Functional electrical stimulation (FES) is an upper limb stroke rehabilitation technology that can enable patients to recover their lost movement by assisting functional task training. Unfortunately, current FES controllers cannot simultaneously satisfy the competing demands of high accuracy, robustness to modelling error and minimal set-up/identification time that are 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 and learning properties of existing FES controllers. A practical design procedure guaranteeing robust performance is developed, and initial experimental results are then presented to confirm efficacy of the approach.