Parametrised Function ILC with application to FES Electrode Arrays
Parametrised Function ILC with application to FES Electrode Arrays
Functional electrical stimulation (FES) is an effective approach to regain lost movement in paralysed or impaired subjects. FES arrays can achieve functional multi-joint angular motion by activating a large number of FES elements. However, their control is challenging due to the need for high precision but the lack of a model or available identification time in a clinical or home setting. This paper develops an approach to 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. It uses a parameterised model form that adapts to new data and replaces identification tests while maintaining accuracy. Results show that 4 references can be tracked using only 10.8% of the experimental tests required by conventional ILC approaches.
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Sun, Xiaoru
f023210d-4ef1-4272-a08d-8d47f2d8ba47
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
5 September 2022
Sun, Xiaoru
f023210d-4ef1-4272-a08d-8d47f2d8ba47
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Sun, Xiaoru and Freeman, Christopher
(2022)
Parametrised Function ILC with application to FES Electrode Arrays.
In Proceedings of the 2022 IEEE American Control Conference.
.
(doi:10.23919/ACC53348.2022.9867790).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Functional electrical stimulation (FES) is an effective approach to regain lost movement in paralysed or impaired subjects. FES arrays can achieve functional multi-joint angular motion by activating a large number of FES elements. However, their control is challenging due to the need for high precision but the lack of a model or available identification time in a clinical or home setting. This paper develops an approach to 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. It uses a parameterised model form that adapts to new data and replaces identification tests while maintaining accuracy. Results show that 4 references can be tracked using only 10.8% of the experimental tests required by conventional ILC approaches.
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Accepted/In Press date: 31 January 2022
Published date: 5 September 2022
Identifiers
Local EPrints ID: 454810
URI: http://eprints.soton.ac.uk/id/eprint/454810
PURE UUID: c3171b73-c65a-49b3-bd12-f331344d16ad
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Date deposited: 24 Feb 2022 21:49
Last modified: 14 Aug 2025 01:38
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Author:
Xiaoru Sun
Author:
Christopher Freeman
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