Neural network based ILC with application to FES electrode arrays

Sun, Xiaoru and Freeman, Chris (2024) Neural network based ILC with application to FES electrode arrays. 2024 American Control Conference, Westin Harbour Castle, Toronto, Canada. 10 - 12 Jul 2024. 6 pp . (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

Functional electrical stimulation (FES) is a technology that can help paralysed or impaired subjects regain their lost movement after stroke. A large number of FES elements can be combined to form FES arrays which are capable of activating the multiple muscles needed to perform functional arm movement. However, the control of FES arrays is challenging since high precision is required but there is little time available in a clinical or home setting to identify a model. To address
this problem, an approach is developed that can deliver high accuracy with minimal identification overhead. It is based on iterative learning control (ILC), a technique that exploits the repeated nature of rehabilitation training. The method uses a parameterised model form that adapts to new data and replaces identification tests while maintaining accuracy. Conditions are derived to ensure convergence is preserved while minimising identification time. Numerical results show that four references can be tracked using only 10.8% of the experimental tests required by conventional ILC algorithms. The approach is then applied experimentally to four unimpaired subjects using a realistic rehabilitation scenario, with results showing mean tracking accuracy within 5, while requiring only between 25% and 64:9% of the experimental tests of conventional ILC.

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