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Norm optimal iterative learning control based on a multiple model switched adaptive framework

Brend, O., Freeman, C.T. and French, M. (2013) Norm optimal iterative learning control based on a multiple model switched adaptive framework At IEEE 52nd Annual Conference on Decision and Control, Italy. 10 - 13 Dec 2013. 6 pp, pp. 7297-7302.

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper a prominent class of iterative learning control (ILC) algorithm is reformulated in the framework of estimation-based multiple model switched adaptive control (EMMSAC). The resulting control scheme uses a bank of Kalman filters to assess the performance of a set of candidate plant models, and the ILC update at the end of each trial is constructed using the plant model with smallest residual. Through exploitation of the powerful underlying EMMSAC framework, rigorous bounds are available to guarantee robust ILC performance without placing constraints on the form of uncertainty or control action.

This paper hence addresses current limitations in ILC approaches for uncertain systems which are typically highly restrictive in the form or magnitude of the uncertainty, employ prescribed controller forms, or, alternatively are heuristically motivated with no theoretical stability/performance guarantees. Experimental results from a highly relevant application of ILC in stroke rehabilitation are given to confirm the efficacy and scope of the framework.

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More information

Published date: 1 October 2013
Venue - Dates: IEEE 52nd Annual Conference on Decision and Control, Italy, 2013-12-10 - 2013-12-13
Organisations: EEE, Southampton Wireless Group

Identifiers

Local EPrints ID: 354854
URI: http://eprints.soton.ac.uk/id/eprint/354854
PURE UUID: c199e40e-3e76-4059-a781-c149b1e8d274

Catalogue record

Date deposited: 19 Jul 2013 19:33
Last modified: 18 Jul 2017 03:52

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Contributors

Author: O. Brend
Author: C.T. Freeman
Author: M. French

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