Model and Experience-based initial input construction for iterative learning control
Model and Experience-based initial input construction for iterative learning control
The initial choice of input in iterative learning control (ILC) generally has a significant effect on the error incurred over subsequent trials. In this paper techniques are developed which use experimental data gathered over previous applications of ILC in order to generate an initial input signal for the tracking of a new reference trajectory. A model-based approach is then incorporated to overcome the limitation of insufficient previous experimental data, and a robust design procedure is developed. Experimental evaluation results are obtained using a gantry robot facility.
430-447
Freeman, Chris
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Alsubaie, Muhammad
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Cai, Zhonglun
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Rogers, Eric
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Lewin, Paul
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2011
Freeman, Chris
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Alsubaie, Muhammad
9b05bc31-3ed9-4404-890d-534cad10921e
Cai, Zhonglun
dd8dd525-19a5-4792-a048-617340996afe
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Lewin, Paul
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Freeman, Chris, Alsubaie, Muhammad, Cai, Zhonglun, Rogers, Eric and Lewin, Paul
(2011)
Model and Experience-based initial input construction for iterative learning control.
International Journal of Adaptive Control and Signal Processing, 25 (5), .
Abstract
The initial choice of input in iterative learning control (ILC) generally has a significant effect on the error incurred over subsequent trials. In this paper techniques are developed which use experimental data gathered over previous applications of ILC in order to generate an initial input signal for the tracking of a new reference trajectory. A model-based approach is then incorporated to overcome the limitation of insufficient previous experimental data, and a robust design procedure is developed. Experimental evaluation results are obtained using a gantry robot facility.
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Published date: 2011
Organisations:
EEE, Southampton Wireless Group
Identifiers
Local EPrints ID: 268451
URI: http://eprints.soton.ac.uk/id/eprint/268451
ISSN: 0890-6327
PURE UUID: 7ba03ed9-abd4-4a6c-a0be-f468e542363f
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Date deposited: 02 Feb 2010 00:17
Last modified: 15 Mar 2024 02:43
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Contributors
Author:
Chris Freeman
Author:
Muhammad Alsubaie
Author:
Zhonglun Cai
Author:
Eric Rogers
Author:
Paul Lewin
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