Multiple model iterative learning control with application to upper limb stroke rehabilitation
Multiple model iterative learning control with application to upper limb stroke rehabilitation
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.
identification, iterative learning control, multiple-model, stroke rehabilitation, upper-limb
Zhou, Junlin
4e4b04ca-2dc8-4a83-bee1-06e45e797e01
Freeman, Christopher T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Holderbaum, William
f058d665-b418-463e-a42e-7fcb8382154c
2023
Zhou, Junlin
4e4b04ca-2dc8-4a83-bee1-06e45e797e01
Freeman, Christopher T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Holderbaum, William
f058d665-b418-463e-a42e-7fcb8382154c
Zhou, Junlin, Freeman, Christopher T. and Holderbaum, William
(2023)
Multiple model iterative learning control with application to upper limb stroke rehabilitation.
In 2023 International Interdisciplinary PhD Workshop, IIPhDW 2023.
IEEE.
4 pp
.
(doi:10.1109/IIPhDW54739.2023.10124411).
Record type:
Conference or Workshop Item
(Paper)
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.
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More information
Accepted/In Press date: 1 April 2023
e-pub ahead of print date: 17 May 2023
Published date: 2023
Additional Information:
Publisher Copyright:
© 2023 IEEE.
Venue - Dates:
International Interdisciplinary PhD Workshop, Wismar, Germany, 2023-05-03 - 2023-05-05
Keywords:
identification, iterative learning control, multiple-model, stroke rehabilitation, upper-limb
Identifiers
Local EPrints ID: 484834
URI: http://eprints.soton.ac.uk/id/eprint/484834
PURE UUID: 9bc26859-af8c-47bc-84a6-9e1459bdd9eb
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Date deposited: 22 Nov 2023 17:50
Last modified: 17 Mar 2024 02:56
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Contributors
Author:
Junlin Zhou
Author:
Christopher T. Freeman
Author:
William Holderbaum
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