Multiple model adaptive ILC for human movement assistance
Multiple model adaptive ILC for human movement assistance
A switched multiple model iterative learning control framework is developed which guarantees robust stability and performance bounds under the assumption that the true plant belongs to a plant uncertainty set that is specified by the designer. In addition, the framework automatically adapts the reference trajectory according to the action of an existing internal control loop that is assumed to be embedded in the plant structure. The framework is inspired by the needs of stroke rehabilitation where assistive technology must support the remaining, weak volitional effort of the patient. Exploiting the multiple model based switching between models and reference trajectories, the framework is also able to potentially eliminate the need for identification and tuning and hence meet the demanding needs of clinical application.
1-6
Freeman, Christopher
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Spraggs, Matthew W.
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Hughes, Ann-Marie
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Yang, Kai
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Tudor, Michael
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Grabham, Neil
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12 June 2018
Freeman, Christopher
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Spraggs, Matthew W.
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Hughes, Ann-Marie
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Yang, Kai
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Tudor, Michael
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Grabham, Neil
00695728-6280-4d06-a943-29142f2547c9
Freeman, Christopher, Spraggs, Matthew W., Hughes, Ann-Marie, Yang, Kai, Tudor, Michael and Grabham, Neil
(2018)
Multiple model adaptive ILC for human movement assistance.
IFAC European Control Conference 2018, , Limassol, Cyprus.
12 - 15 Jun 2018.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
A switched multiple model iterative learning control framework is developed which guarantees robust stability and performance bounds under the assumption that the true plant belongs to a plant uncertainty set that is specified by the designer. In addition, the framework automatically adapts the reference trajectory according to the action of an existing internal control loop that is assumed to be embedded in the plant structure. The framework is inspired by the needs of stroke rehabilitation where assistive technology must support the remaining, weak volitional effort of the patient. Exploiting the multiple model based switching between models and reference trajectories, the framework is also able to potentially eliminate the need for identification and tuning and hence meet the demanding needs of clinical application.
Text
ECC_taskadaption4_final
More information
Published date: 12 June 2018
Venue - Dates:
IFAC European Control Conference 2018, , Limassol, Cyprus, 2018-06-12 - 2018-06-15
Identifiers
Local EPrints ID: 426374
URI: http://eprints.soton.ac.uk/id/eprint/426374
PURE UUID: 46d4a6ff-2872-4ac9-990d-562636ea997e
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Date deposited: 26 Nov 2018 17:30
Last modified: 16 Mar 2024 04:03
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Contributors
Author:
Christopher Freeman
Author:
Matthew W. Spraggs
Author:
Michael Tudor
Author:
Neil Grabham
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