Multiple model iterative learning control of FES electrode arrays

Abstract

Stroke is a common cause of hand and upper limb disability, but current rehabilitation approaches do not adequately support successful recovery. Functional electrical stimulation (FES) is the most widely used assistive technology, and is able to support accurate hand and wrist motion when applied using multi-element electrode arrays. However, accurate movements have only been possible using an iterative learning control (ILC) approach involving many repeated model identification tests. This lengthy process limits wide-spread use. This paper presents a solution for FES electrode array control using estimation-based multiple-model ILC (EM-MILC), in which a set of parameterised models is used to automatically update the stimulation applied to each array element every time a task is carried out. This removes the need for model identification, significantly improving system usability whilst maintaining high performance. Experimental results demonstrate that EM-MILC reduces the average number of tests from 16 to 3, compared to the most accurate existing approach.

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