Published December 2005
| public
Book Section - Chapter
On Model Reduction of Polynomial Dynamical Systems
- Creators
- Prajna, Stephen
- Sandberg, Henrik
Abstract
In this paper, we develop a computational method for model reduction of polynomial dynamical systems. This is achieved using sum of squares relaxations on certain Lyapunov inequalities, which are the nonlinear counterparts of the Lyapunov controllability and observability linear matrix inequalities for linear systems. In our model reduction procedure, we use notions of balanced realization and balanced truncation for a polynomial model. In addition, we derive an a-priori error bound on the approximation error for balanced truncation.
Additional Information
© 2005 IEEE. Date of Current Version: 30 January 2006. The work of the first author was financially supported by the Army Institute for Collaborative Biotechnologies, the NSF Award CCF-0326635, and the AFOSR Award FA9550-05-1-0032. The work of the second author was financially supported by the Swedish Research Council (project 2000-5630) and the Swedish Foundation for Strategic Research (project CPDC).Additional details
- Eprint ID
- 25100
- DOI
- 10.1109/CDC.2005.1582398
- Resolver ID
- CaltechAUTHORS:20110825-135418098
- Army Institute for Collaborative Biotechnologies
- CCF-0326635
- NSF
- FA9550-05-1-0032
- Air Force Office of Scientific Research (AFOSR)
- 2000-5630
- Swedish Foundation for Strategic Research
- Swedish Foundation for Strategic Research (CPDC)
- Created
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2011-08-25Created from EPrint's datestamp field
- Updated
-
2021-11-09Created from EPrint's last_modified field
- Series Name
- IEEE Conference on Decision and Control Proceedings