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Evaluation of iterative learning control using a multivariable test facility

Evaluation of iterative learning control using a multivariable test facility
Evaluation of iterative learning control using a multivariable test facility
Iterative learning control (ILC) is an approach suitable for systems which repeatedly perform a tracking task over a fixed time interval. However little attention has been paid to the case of multiple input, multiple output (MIMO) systems. In this paper theoretical results are derived and establish a close link between increased interaction, reduced robustness, slower convergence and greater control effort. Focusing on the popular class of norm optimal ILC (NOILC) algorithms, these Findings are experimentally confirmed using a MIMO test facility which permits both exogenous disturbance injection and a variable level of coupling between input and output pairs. To address performance limitations 'point-to-point' NOILC is then introduced, in which the need to track the reference at all time points along the trial is relaxed.
146-151
Dinh Van, Thanh
01c5b7fd-b205-4e90-a11a-1c811c02d9ae
Freeman, C T
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Lewin, P.L.
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Dinh Van, Thanh
01c5b7fd-b205-4e90-a11a-1c811c02d9ae
Freeman, C T
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Lewin, P.L.
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e

Dinh Van, Thanh, Freeman, C T and Lewin, P.L. (2013) Evaluation of iterative learning control using a multivariable test facility. 5th IFAC International Workshop on Adaption and Learning. 03 - 05 Jul 2013. pp. 146-151 .

Record type: Conference or Workshop Item (Paper)

Abstract

Iterative learning control (ILC) is an approach suitable for systems which repeatedly perform a tracking task over a fixed time interval. However little attention has been paid to the case of multiple input, multiple output (MIMO) systems. In this paper theoretical results are derived and establish a close link between increased interaction, reduced robustness, slower convergence and greater control effort. Focusing on the popular class of norm optimal ILC (NOILC) algorithms, these Findings are experimentally confirmed using a MIMO test facility which permits both exogenous disturbance injection and a variable level of coupling between input and output pairs. To address performance limitations 'point-to-point' NOILC is then introduced, in which the need to track the reference at all time points along the trial is relaxed.

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Published date: 3 July 2013
Venue - Dates: 5th IFAC International Workshop on Adaption and Learning, 2013-07-03 - 2013-07-05
Organisations: EEE

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Local EPrints ID: 346295
URI: http://eprints.soton.ac.uk/id/eprint/346295
PURE UUID: ea3a0314-a15b-4f8e-a561-d3f7b8b769ee

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Date deposited: 16 Dec 2012 15:34
Last modified: 19 Jul 2019 21:47

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