Sparsity-promoting dynamic mode decomposition for systems with inputs

Jennifer Annoni, Peter Seiler, Mihailo R. Jovanovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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 languageEnglish (US)
Title of host publication2016 IEEE 55th Conference on Decision and Control, CDC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6506-6511
Number of pages6
ISBN (Electronic)9781509018376
DOIs
StatePublished - Dec 27 2016
Event55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States
Duration: Dec 12 2016Dec 14 2016

Publication series

Name2016 IEEE 55th Conference on Decision and Control, CDC 2016

Other

Other55th IEEE Conference on Decision and Control, CDC 2016
Country/TerritoryUnited States
CityLas Vegas
Period12/12/1612/14/16

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

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