A 2D systems approach to iterative learning control for discrete linear processes with zero Markov parameters
A 2D systems approach to iterative learning control for discrete linear processes with zero Markov parameters
In this paper a new approach to iterative learning control for the practically relevant case of deterministic discrete linear plants with uniform rank greater than unity is developed. The analysis is undertaken in a 2D systems setting that, by using a strong form of stability for linear repetitive processes, allows simultaneous consideration of both trial-to-trial error convergence and along the trial performance, resulting in design algorithms that can be computed using Linear Matrix Inequalities (LMIs). Finally, the control laws are experimentally verified on a gantry robot that replicates a pick and place operation commonly found in a number of applications to which iterative learning control is applicable.
1246-1262
Hladowski, L
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Galkowski, K
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Cai, Z
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Rogers, E
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Freeman, C T
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Lewin, P L
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2 August 2011
Hladowski, L
c557e4e1-c08c-4fb6-84d8-909f15786fcb
Galkowski, K
65b638be-b5a5-4e25-b1b8-e152c08a1cbb
Cai, Z
dd8dd525-19a5-4792-a048-617340996afe
Rogers, E
611b1de0-c505-472e-a03f-c5294c63bb72
Freeman, C T
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Lewin, P L
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Hladowski, L, Galkowski, K, Cai, Z, Rogers, E, Freeman, C T and Lewin, P L
(2011)
A 2D systems approach to iterative learning control for discrete linear processes with zero Markov parameters.
International Journal of Control, 84 (7), .
Abstract
In this paper a new approach to iterative learning control for the practically relevant case of deterministic discrete linear plants with uniform rank greater than unity is developed. The analysis is undertaken in a 2D systems setting that, by using a strong form of stability for linear repetitive processes, allows simultaneous consideration of both trial-to-trial error convergence and along the trial performance, resulting in design algorithms that can be computed using Linear Matrix Inequalities (LMIs). Finally, the control laws are experimentally verified on a gantry robot that replicates a pick and place operation commonly found in a number of applications to which iterative learning control is applicable.
More information
Published date: 2 August 2011
Organisations:
EEE, Southampton Wireless Group
Identifiers
Local EPrints ID: 272145
URI: http://eprints.soton.ac.uk/id/eprint/272145
ISSN: 0020-3270
PURE UUID: dfd3dae9-28cc-4474-8468-530328acb031
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Date deposited: 05 Apr 2011 15:53
Last modified: 11 Dec 2024 02:39
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Contributors
Author:
L Hladowski
Author:
K Galkowski
Author:
Z Cai
Author:
E Rogers
Author:
C T Freeman
Author:
P L Lewin
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