Model Reduction Via Balanced Realizations: An Extension and Frequency Weighting Techniques

Ubaid M. Al-Saggaf; Gene F. Franklin

doi:10.1109/9.1280

Model Reduction Via Balanced Realizations: An Extension and Frequency Weighting Techniques

Ubaid M. Al-Saggaf, Gene F. Franklin

King Fahd University of Petroleum & Minerals

Research output: Contribution to journal › Article › peer-review

114 Scopus citations

Abstract

Two model reduction methods for discrete systems related to balanced realizations are described. The first is a technique which utilizes the least controllable and observable subsystem in deriving a balanced discrete reduced-order model that has some additional nice properties. For this technique an L∞ norm bound on the reduction error is given. The second method is a new frequency weighting technique for discrete- and continuous-time systems where the input-normal or output-normal realizations are modified to include a simple frequency weighting. Also for this technique L∞ norm bounds on the weighted reduction errors are obtained.

Original language	English
Pages (from-to)	687-692
Number of pages	6
Journal	IEEE Transactions on Automatic Control
Volume	33
Issue number	7
DOIs	https://doi.org/10.1109/9.1280
State	Published - Jul 1988

Bibliographical note

Funding Information:
Manuscript received July 24, 1987; revised October 23, 1987. This work was supported by King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. U. M. AI-Saggaf is with the Department of Electrical Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. G. F. Franklin is with the Information Systems Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA 94305. IEEE Log Number 8821011.

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

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Cite this

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title = "Model Reduction Via Balanced Realizations: An Extension and Frequency Weighting Techniques",

abstract = "Two model reduction methods for discrete systems related to balanced realizations are described. The first is a technique which utilizes the least controllable and observable subsystem in deriving a balanced discrete reduced-order model that has some additional nice properties. For this technique an L∞ norm bound on the reduction error is given. The second method is a new frequency weighting technique for discrete- and continuous-time systems where the input-normal or output-normal realizations are modified to include a simple frequency weighting. Also for this technique L∞ norm bounds on the weighted reduction errors are obtained.",

author = "Al-Saggaf, \{Ubaid M.\} and Franklin, \{Gene F.\}",

note = "Funding Information: Manuscript received July 24, 1987; revised October 23, 1987. This work was supported by King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. U. M. AI-Saggaf is with the Department of Electrical Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. G. F. Franklin is with the Information Systems Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA 94305. IEEE Log Number 8821011.",

year = "1988",

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N2 - Two model reduction methods for discrete systems related to balanced realizations are described. The first is a technique which utilizes the least controllable and observable subsystem in deriving a balanced discrete reduced-order model that has some additional nice properties. For this technique an L∞ norm bound on the reduction error is given. The second method is a new frequency weighting technique for discrete- and continuous-time systems where the input-normal or output-normal realizations are modified to include a simple frequency weighting. Also for this technique L∞ norm bounds on the weighted reduction errors are obtained.

AB - Two model reduction methods for discrete systems related to balanced realizations are described. The first is a technique which utilizes the least controllable and observable subsystem in deriving a balanced discrete reduced-order model that has some additional nice properties. For this technique an L∞ norm bound on the reduction error is given. The second method is a new frequency weighting technique for discrete- and continuous-time systems where the input-normal or output-normal realizations are modified to include a simple frequency weighting. Also for this technique L∞ norm bounds on the weighted reduction errors are obtained.

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Model Reduction Via Balanced Realizations: An Extension and Frequency Weighting Techniques

Abstract

Bibliographical note

ASJC Scopus subject areas

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