Multiple model adaptive control of functional electrical stimulation
Multiple model adaptive control of functional electrical stimulation
This paper establishes the feasibility of multiple-model switched adaptive control to regulate functional electrical stimulation for upper limb stroke rehabilitation. An estimation-based multiple-model switched adaptive control (EMMSAC) framework for nonlinear time-invariant systems is described, and extensions are presented to enable application to time-varying Hammerstein structures that can accurately represent the stimulated arm. A principled design procedure is then developed to construct both a suitable set of candidate models from experimental data and a corresponding set of tracking controllers. The procedure is applied to a sample of able-bodied young participants to produce a general EMMSAC controller. This is then applied to a different sample of the population during an isometric nonvoluntary trajectory tracking task. The results show that it is possible to eliminate model identification while employing closed-loop controllers that maintain high performance in the presence of rapidly changing system dynamics. This paper hence addresses critical limitations to effective stroke rehabilitation in a clinical setting.
1901-1913
Brend, O.
0932d595-1da7-4a16-8e7e-2d065a058866
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
French, M.
22958f0e-d779-4999-adf6-2711e2d910f8
September 2015
Brend, O.
0932d595-1da7-4a16-8e7e-2d065a058866
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
French, M.
22958f0e-d779-4999-adf6-2711e2d910f8
Brend, O., Freeman, C.T. and French, M.
(2015)
Multiple model adaptive control of functional electrical stimulation.
IEEE Transactions on Control Systems Technology, 23 (5), .
(doi:10.1109/TCST.2015.2394508).
Abstract
This paper establishes the feasibility of multiple-model switched adaptive control to regulate functional electrical stimulation for upper limb stroke rehabilitation. An estimation-based multiple-model switched adaptive control (EMMSAC) framework for nonlinear time-invariant systems is described, and extensions are presented to enable application to time-varying Hammerstein structures that can accurately represent the stimulated arm. A principled design procedure is then developed to construct both a suitable set of candidate models from experimental data and a corresponding set of tracking controllers. The procedure is applied to a sample of able-bodied young participants to produce a general EMMSAC controller. This is then applied to a different sample of the population during an isometric nonvoluntary trajectory tracking task. The results show that it is possible to eliminate model identification while employing closed-loop controllers that maintain high performance in the presence of rapidly changing system dynamics. This paper hence addresses critical limitations to effective stroke rehabilitation in a clinical setting.
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e-pub ahead of print date: 20 May 2015
Published date: September 2015
Organisations:
Vision, Learning and Control, EEE
Identifiers
Local EPrints ID: 361362
URI: http://eprints.soton.ac.uk/id/eprint/361362
ISSN: 1063-6536
PURE UUID: 3f0f218b-d6d4-48c9-891e-6b5e6c02d474
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Date deposited: 18 Jan 2014 15:35
Last modified: 11 Dec 2024 02:39
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Author:
O. Brend
Author:
C.T. Freeman
Author:
M. French
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