H2-Galerkin projection method for model order reduction of linear and nonlinear systems

Salim Ibrir

doi:10.1109/CDC.2017.8264219

H₂-Galerkin projection method for model order reduction of linear and nonlinear systems

Salim Ibrir^*

^*Corresponding author for this work

King Fahd University of Petroleum & Minerals

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

A new Model-order Reduction (MoR) procedure, that combines Galerkin projection method with H₂-norm minimization technique, is developed for linear continuous-time systems. The proposed algorithm, stated as the solution of a set of Linear-Matrix-Inequality (LMI) conditions, is subsequently utilized in MoR of nonlinear systems with stability preservation. The developed algorithms are dedicated to stable MIMO linear systems and stable MIMO nonlinear systems having repetitive nonlinearities. The obtained results are compared to the classical balanced truncation algorithm, the Hankel MoR method, and the recent incremental balanced truncation procedure. The designs are illustrated through many examples including a case of a nonlinear high-order electrical circuit.

Original language	English
Title of host publication	2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3805-3810
Number of pages	6
ISBN (Electronic)	9781509028733
DOIs	https://doi.org/10.1109/CDC.2017.8264219
State	Published - 28 Jun 2017

Publication series

Name	2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Volume	2018-January

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

ASJC Scopus subject areas

Decision Sciences (miscellaneous)
Industrial and Manufacturing Engineering
Control and Optimization

Access to Document

10.1109/CDC.2017.8264219

Cite this

Ibrir, S. (2017). H₂-Galerkin projection method for model order reduction of linear and nonlinear systems. In 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017 (pp. 3805-3810). (2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2017.8264219

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title = "H2-Galerkin projection method for model order reduction of linear and nonlinear systems",

abstract = "A new Model-order Reduction (MoR) procedure, that combines Galerkin projection method with H2-norm minimization technique, is developed for linear continuous-time systems. The proposed algorithm, stated as the solution of a set of Linear-Matrix-Inequality (LMI) conditions, is subsequently utilized in MoR of nonlinear systems with stability preservation. The developed algorithms are dedicated to stable MIMO linear systems and stable MIMO nonlinear systems having repetitive nonlinearities. The obtained results are compared to the classical balanced truncation algorithm, the Hankel MoR method, and the recent incremental balanced truncation procedure. The designs are illustrated through many examples including a case of a nonlinear high-order electrical circuit.",

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Ibrir, S 2017, H₂-Galerkin projection method for model order reduction of linear and nonlinear systems. in 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 3805-3810. https://doi.org/10.1109/CDC.2017.8264219

H₂-Galerkin projection method for model order reduction of linear and nonlinear systems. / Ibrir, Salim.
2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 3805-3810 (2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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