Modeling the influence of non-minimum phase zeros on gradient based linear iterative learning control
Modeling the influence of non-minimum phase zeros on gradient based linear iterative learning control
The subject of this paper is modeling of the influence of non-minimum phase plant dynamics on the performance possible from gradient based norm optimal iterative learning control algorithms. It is established that performance in the presence of right-half plane plant zeros typically has two phases. These consist of an initial fast monotonic reduction of the L 2 error norm followed by a very slow asymptotic convergence. Although the norm of the tracking error does eventually converge to zero, the practical implications over finite trials is apparent convergence to a non-zero error. The source of this slow convergence is identified and a model of this behavior as a (set of) linear constraint(s) is developed. This is shown to provide a good prediction of the magnitude of error norm where slow convergence begins. Formulae for this norm are obtained for single-input single-output systems with several right half plane zeroes using Lagrangian techniques and experimental results are given that confirm the practical validity of the analysis.
Owens, David H.
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Chu, Bing
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Cai, Zhonglun
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Rogers, Eric
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Freeman, Chris T.
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Lewin, Paul L.
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28 October 2010
Owens, David H.
dca0ba32-aba6-4bab-a511-9bd322da16df
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Cai, Zhonglun
282d7451-72d0-4476-8571-f2141d5694e8
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Freeman, Chris T.
c024cdf6-024f-4f91-a802-8d1d87546d60
Lewin, Paul L.
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Owens, David H., Chu, Bing, Cai, Zhonglun, Rogers, Eric, Freeman, Chris T. and Lewin, Paul L.
(2010)
Modeling the influence of non-minimum phase zeros on gradient based linear iterative learning control.
In 2010 IEEE International Conference on Control Applications (CCA 2010).
IEEE.
6 pp
.
(doi:10.1109/CCA.2010.5611338).
Record type:
Conference or Workshop Item
(Paper)
Abstract
The subject of this paper is modeling of the influence of non-minimum phase plant dynamics on the performance possible from gradient based norm optimal iterative learning control algorithms. It is established that performance in the presence of right-half plane plant zeros typically has two phases. These consist of an initial fast monotonic reduction of the L 2 error norm followed by a very slow asymptotic convergence. Although the norm of the tracking error does eventually converge to zero, the practical implications over finite trials is apparent convergence to a non-zero error. The source of this slow convergence is identified and a model of this behavior as a (set of) linear constraint(s) is developed. This is shown to provide a good prediction of the magnitude of error norm where slow convergence begins. Formulae for this norm are obtained for single-input single-output systems with several right half plane zeroes using Lagrangian techniques and experimental results are given that confirm the practical validity of the analysis.
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Published date: 28 October 2010
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Local EPrints ID: 472395
URI: http://eprints.soton.ac.uk/id/eprint/472395
PURE UUID: 243fd8d2-9853-4520-b995-6af98c24fed0
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Date deposited: 05 Dec 2022 17:32
Last modified: 17 Mar 2024 03:28
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Contributors
Author:
David H. Owens
Author:
Bing Chu
Author:
Zhonglun Cai
Author:
Eric Rogers
Author:
Chris T. Freeman
Author:
Paul L. Lewin
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