Input-output structure decomposition for multivariable control system by genetic algorithm

L. F. Yeung; K. Y. Chan; D. Z. Liao

Input-output structure decomposition for multivariable control system by genetic algorithm

L. F. Yeung
, K. Y. Chan
, D. Z. Liao

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

In this paper, a input-output weighting matrix is proposed as the coupling measure between the input variables to the output variables, From the structure of the input-output weighting matrix, we can reduce the inter-channel coupling by permutation and blocking. This paper shows how to use this information to identify the strongly and weakly coupled sub-systems by setting the partition of the input-output weighting matrix. A Genetic Algorithm for this application is proposed to determine the best permutation and the number of the partition, so that the inter-channel coupling will be deduced after rearranging the input/output variables.

Original language	English
Title of host publication	Proceedings of the 4th Asia-Pacific Conference on Control and Measurement
Pages	108-113
Publication status	Published - 2000
Event	Proceedings of the 4th Asia-Pacific Conference on Control and Measurement - Guilin, China Duration: 9 Jul 2000 → 12 Jul 2000

Conference

Conference	Proceedings of the 4th Asia-Pacific Conference on Control and Measurement
Place	China
City	Guilin
Period	9/07/00 → 12/07/00

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@inproceedings{0faa9d16ab1548d7b740d152d7565d43,

title = "Input-output structure decomposition for multivariable control system by genetic algorithm",

abstract = "In this paper, a input-output weighting matrix is proposed as the coupling measure between the input variables to the output variables, From the structure of the input-output weighting matrix, we can reduce the inter-channel coupling by permutation and blocking. This paper shows how to use this information to identify the strongly and weakly coupled sub-systems by setting the partition of the input-output weighting matrix. A Genetic Algorithm for this application is proposed to determine the best permutation and the number of the partition, so that the inter-channel coupling will be deduced after rearranging the input/output variables.",

author = "Yeung, \{L. F.\} and Chan, \{K. Y.\} and Liao, \{D. Z.\}",

year = "2000",

language = "English",

isbn = "7801346955",

pages = "108--113",

booktitle = "Proceedings of the 4th Asia-Pacific Conference on Control and Measurement",

note = "Proceedings of the 4th Asia-Pacific Conference on Control and Measurement ; Conference date: 09-07-2000 Through 12-07-2000",

}

Input-output structure decomposition for multivariable control system by genetic algorithm. / Yeung, L. F.; Chan, K. Y.; Liao, D. Z.
Proceedings of the 4th Asia-Pacific Conference on Control and Measurement. 2000. p. 108-113.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Input-output structure decomposition for multivariable control system by genetic algorithm

AU - Yeung, L. F.

AU - Chan, K. Y.

AU - Liao, D. Z.

PY - 2000

Y1 - 2000

N2 - In this paper, a input-output weighting matrix is proposed as the coupling measure between the input variables to the output variables, From the structure of the input-output weighting matrix, we can reduce the inter-channel coupling by permutation and blocking. This paper shows how to use this information to identify the strongly and weakly coupled sub-systems by setting the partition of the input-output weighting matrix. A Genetic Algorithm for this application is proposed to determine the best permutation and the number of the partition, so that the inter-channel coupling will be deduced after rearranging the input/output variables.

AB - In this paper, a input-output weighting matrix is proposed as the coupling measure between the input variables to the output variables, From the structure of the input-output weighting matrix, we can reduce the inter-channel coupling by permutation and blocking. This paper shows how to use this information to identify the strongly and weakly coupled sub-systems by setting the partition of the input-output weighting matrix. A Genetic Algorithm for this application is proposed to determine the best permutation and the number of the partition, so that the inter-channel coupling will be deduced after rearranging the input/output variables.

UR - https://www.scopus.com/pages/publications/0034590599

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0034590599&origin=recordpage

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 7801346955

SP - 108

EP - 113

BT - Proceedings of the 4th Asia-Pacific Conference on Control and Measurement

T2 - Proceedings of the 4th Asia-Pacific Conference on Control and Measurement

Y2 - 9 July 2000 through 12 July 2000

ER -

Input-output structure decomposition for multivariable control system by genetic algorithm

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