Multiple model switched repetitive control for tremor suppression
Multiple model switched repetitive control for tremor suppression
Tremor is a condition that impacts millions of people globally, and is characterised by a periodic limb movement that impedes voluntary motion. Recent studies have shown that functional electrical stimulation (FES) can help reduce tremor by artificially stimulating opposing muscles, thereby decreasing the oscillation’s amplitude. Various control methods have been proposed for this purpose, but repetitive control (RC) has shown the most promise with potential to completely suppress the tremor. While several RC approaches have demonstrated suppression rates of up to 90%, they heavily rely on an accurate model of the underlying dynamics, and their effectiveness declines steeply due to factors like muscle fatigue, spasticity, and modelling inaccuracies.
This paper introduces a multiple model switched repetitive control (MMSRC) framework that addresses the limitations of existing RC approaches. It guarantees high performance tremor suppression provided the true dynamics belong to an uncertainty set specified by the designer. This enables it to adapt to time-varying physiological changes, as well as changes in the placement of the FES electrodes. Moreover, once an uncertainty set has been established, it removes the need for subsequent model identification. This is an important step towards home-based tremor suppression where model identification and expert tuning are not possible. Experimental validation is performed with four participants, showing that MMSRC effectively suppresses tremor even in the presence of severe modelling uncertainty and fatigue, unlike conventional RC methods which often become unstable under these conditions.
Multiple model switched adaptive control, Repetitive control, Tremor suppression
103392
Fang, Tingze
32b92c13-c0e9-45c8-886b-dfa9a29e4c73
Freeman, Christopher T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Fang, Tingze
32b92c13-c0e9-45c8-886b-dfa9a29e4c73
Freeman, Christopher T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Abstract
Tremor is a condition that impacts millions of people globally, and is characterised by a periodic limb movement that impedes voluntary motion. Recent studies have shown that functional electrical stimulation (FES) can help reduce tremor by artificially stimulating opposing muscles, thereby decreasing the oscillation’s amplitude. Various control methods have been proposed for this purpose, but repetitive control (RC) has shown the most promise with potential to completely suppress the tremor. While several RC approaches have demonstrated suppression rates of up to 90%, they heavily rely on an accurate model of the underlying dynamics, and their effectiveness declines steeply due to factors like muscle fatigue, spasticity, and modelling inaccuracies.
This paper introduces a multiple model switched repetitive control (MMSRC) framework that addresses the limitations of existing RC approaches. It guarantees high performance tremor suppression provided the true dynamics belong to an uncertainty set specified by the designer. This enables it to adapt to time-varying physiological changes, as well as changes in the placement of the FES electrodes. Moreover, once an uncertainty set has been established, it removes the need for subsequent model identification. This is an important step towards home-based tremor suppression where model identification and expert tuning are not possible. Experimental validation is performed with four participants, showing that MMSRC effectively suppresses tremor even in the presence of severe modelling uncertainty and fatigue, unlike conventional RC methods which often become unstable under these conditions.
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MMSRC_published
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Accepted/In Press date: 21 July 2025
e-pub ahead of print date: 5 August 2025
Keywords:
Multiple model switched adaptive control, Repetitive control, Tremor suppression
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Local EPrints ID: 505044
URI: http://eprints.soton.ac.uk/id/eprint/505044
ISSN: 0957-4158
PURE UUID: 9eea3e83-7957-402c-a532-fa7e08b88140
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Date deposited: 24 Sep 2025 16:55
Last modified: 25 Sep 2025 01:39
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Author:
Tingze Fang
Author:
Christopher T. Freeman
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