Abstract
The objective of this paper is to address the selection of dominant modes of a system that can be used to construct a reduced-order model. This work is motivated by high-fidelity computational models that capture fluid and/or structural dynamics, which are prohibitively complex for real-time control. A variety of techniques for obtaining simplified control-oriented models have been developed, e.g. proper orthogonal decomposition (POD) and dynamic mode decomposition. In this paper, we address the challenge of selecting a few dominant Koopman modes for systems with exogenous inputs. We use a linear channel flow example to demonstrate the utility of our approach and illustrate the advantages relative to alternative techniques for control-oriented modeling.
Original language | English (US) |
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Title of host publication | 2016 IEEE 55th Conference on Decision and Control, CDC 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 6506-6511 |
Number of pages | 6 |
ISBN (Electronic) | 9781509018376 |
DOIs | |
State | Published - Dec 27 2016 |
Event | 55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States Duration: Dec 12 2016 → Dec 14 2016 |
Publication series
Name | 2016 IEEE 55th Conference on Decision and Control, CDC 2016 |
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Other
Other | 55th IEEE Conference on Decision and Control, CDC 2016 |
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Country/Territory | United States |
City | Las Vegas |
Period | 12/12/16 → 12/14/16 |
Bibliographical note
Funding Information:This work was partially supported by the National Science Foundation under Grant No. CMMI-1254129 entitled CAREER: Probabilistic Tools for High Reliability Monitoring and Control of Wind Farms. The first author gratefully acknowledges the financial support from the University of Minnesota through the 2015-16 Doctoral Dissertation Fellowship. The work of M. R. Jovanovic was supported in part by the National Science Foundation under Award CMMI 1363266, the Air Force Office of Scientific Research under Award FA9550-16-1-0009, and the University of Minnesota Informatics Institute Transdisciplinary Faculty Fellowship
Publisher Copyright:
© 2016 IEEE.