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Robust ILC design with application to stroke rehabilitation

Robust ILC design with application to stroke rehabilitation
Robust ILC design with application to stroke rehabilitation
Iterative learning control (ILC) is a design technique which can achieve accurate tracking by learning over repeated task attempts. However, long-term stability remains a critical limitation to widespread application, and to-date robustness analysis has overwhelmingly considered structured uncertainties. This paper substantially expands the scope of existing ILC robustness analysis by addressing unstructured uncertainties, a widely used ILC update class, the presence of a feedback controller, and a general task description that incorporates the most recent expansions in the ILC tracking objective. Gap metric based analysis is applied to ILC by reformulating the finite horizon trial-to-trial feedforward dynamics into an equivalent along-the-trial feedback system, as well as deriving relationships to link their respective gap metric values. The results are used to generate a comprehensive design framework for robust control design of the interacting feedback and ILC loops. This is illustrated via application to rehabilitation engineering, an area where they meet an urgent need for high performance in the presence of significant modeling uncertainty.
0005-1098
270-278
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815

Freeman, C.T. (2017) Robust ILC design with application to stroke rehabilitation. Automatica, 81, 270-278. (doi:10.1016/j.automatica.2017.04.016).

Record type: Article

Abstract

Iterative learning control (ILC) is a design technique which can achieve accurate tracking by learning over repeated task attempts. However, long-term stability remains a critical limitation to widespread application, and to-date robustness analysis has overwhelmingly considered structured uncertainties. This paper substantially expands the scope of existing ILC robustness analysis by addressing unstructured uncertainties, a widely used ILC update class, the presence of a feedback controller, and a general task description that incorporates the most recent expansions in the ILC tracking objective. Gap metric based analysis is applied to ILC by reformulating the finite horizon trial-to-trial feedforward dynamics into an equivalent along-the-trial feedback system, as well as deriving relationships to link their respective gap metric values. The results are used to generate a comprehensive design framework for robust control design of the interacting feedback and ILC loops. This is illustrated via application to rehabilitation engineering, an area where they meet an urgent need for high performance in the presence of significant modeling uncertainty.

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Freeman_automatica_2016_final - Accepted Manuscript
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Submitted date: 1 September 2016
Accepted/In Press date: 15 March 2017
e-pub ahead of print date: 21 April 2017
Published date: 1 July 2017
Organisations: EEE

Identifiers

Local EPrints ID: 401026
URI: http://eprints.soton.ac.uk/id/eprint/401026
ISSN: 0005-1098
PURE UUID: 4b731cfd-d5c7-4571-81d2-70afee76f531

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Date deposited: 01 Oct 2016 13:23
Last modified: 17 Dec 2019 06:37

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