Multiple model iterative learning control for FES-based stroke rehabilitation

Zhou, J., Freeman, C. T. and Holderbaum, W. ORCID: https://orcid.org/0000-0002-1677-9624 (2023) Multiple model iterative learning control for FES-based stroke rehabilitation. In: American Control Conference (ACC 2023). IEEE. ISBN 9798350328066 doi: 10.23919/acc55779.2023.10155894

Abstract/Summary

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.

Additional Information	American Control Conference (ACC) San Diego, USA 31 May 2023 - 02 June 2023
Item Type	Book or Report Section
URI	https://reading-clone.eprints-hosting.org/id/eprint/112557
Item Type	Book or Report Section
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Biomedical Sciences
Additional Information	American Control Conference (ACC) San Diego, USA 31 May 2023 - 02 June 2023
Publisher	IEEE
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