Multiple model iterative learning control with application to upper limb stroke rehabilitation

Zhou, J., Freeman, C. T. and Holderbaum, W. ORCID: https://orcid.org/0000-0002-1677-9624 (2023) Multiple model iterative learning control with application to upper limb stroke rehabilitation. In: 2023 International Interdisciplinary PhD Workshop (IIPhDW), 3-5 May 2023, Wismar, Germany. doi: 10.1109/iiphdw54739.2023.10124411

Abstract/Summary

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.

Item Type	Conference or Workshop Item (Paper)
URI	https://reading-clone.eprints-hosting.org/id/eprint/112081
Item Type	Conference or Workshop Item
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Department of Bio-Engineering
Publisher	IEEE
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