Scalable Positivity Preserving Model Reduction Using Linear Energy Functions
(2012) 51st IEEE Conference on Decision and Control, 2012 p.4285-4290- Abstract
- In this paper, we explore positivity preserving model reduction. The reduction is performed by truncating the states of the original system without balancing in the classical sense. This may result in conservatism, however, this way the physical meaning of the individual states is preserved. The reduced order models can be obtained using simple matrix operations or using distributed optimization methods. Therefore, the developed algorithms can be applied to sparse large-scale systems.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3625954
- author
- Sootla, Aivar LU and Rantzer, Anders LU
- organization
- publishing date
- 2012
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- IEEE 51st Annual Conference on Decision and Control (CDC), 2012
- pages
- 4285 - 4290
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 51st IEEE Conference on Decision and Control, 2012
- conference location
- Maui, Hawaii, United States
- conference dates
- 2012-12-10 - 2012-12-13
- external identifiers
-
- scopus:84874275636
- ISSN
- 0743-1546
- ISBN
- 978-1-4673-2065-8
- DOI
- 10.1109/CDC.2012.6427032
- project
- LCCC
- language
- English
- LU publication?
- yes
- id
- ffda5119-969a-46c8-a12b-279c9ce09db3 (old id 3625954)
- date added to LUP
- 2016-04-01 14:41:29
- date last changed
- 2024-06-04 15:07:14
@inproceedings{ffda5119-969a-46c8-a12b-279c9ce09db3, abstract = {{In this paper, we explore positivity preserving model reduction. The reduction is performed by truncating the states of the original system without balancing in the classical sense. This may result in conservatism, however, this way the physical meaning of the individual states is preserved. The reduced order models can be obtained using simple matrix operations or using distributed optimization methods. Therefore, the developed algorithms can be applied to sparse large-scale systems.}}, author = {{Sootla, Aivar and Rantzer, Anders}}, booktitle = {{IEEE 51st Annual Conference on Decision and Control (CDC), 2012}}, isbn = {{978-1-4673-2065-8}}, issn = {{0743-1546}}, language = {{eng}}, pages = {{4285--4290}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Scalable Positivity Preserving Model Reduction Using Linear Energy Functions}}, url = {{https://lup.lub.lu.se/search/files/4113155/3625957.pdf}}, doi = {{10.1109/CDC.2012.6427032}}, year = {{2012}}, }