Sparsity-promoting dynamic mode decomposition for systems with inputs

Jennifer Annoni; Peter Seiler; Mihailo R. Jovanovic

doi:10.1109/CDC.2016.7799270

Sparsity-promoting dynamic mode decomposition for systems with inputs

Jennifer Annoni
, Peter Seiler
, Mihailo R. Jovanovic

Aerospace Engineering and Mechanics

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

16 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 language	English (US)
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	https://doi.org/10.1109/CDC.2016.7799270
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

Other

Other	55th IEEE Conference on Decision and Control, CDC 2016
Country/Territory	United States
City	Las Vegas
Period	12/12/16 → 12/14/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

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10.1109/CDC.2016.7799270

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Annoni, J., Seiler, P., & Jovanovic, M. R. (2016). Sparsity-promoting dynamic mode decomposition for systems with inputs. In 2016 IEEE 55th Conference on Decision and Control, CDC 2016 (pp. 6506-6511). Article 7799270 (2016 IEEE 55th Conference on Decision and Control, CDC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2016.7799270

Sparsity-promoting dynamic mode decomposition for systems with inputs. / Annoni, Jennifer; Seiler, Peter; Jovanovic, Mihailo R.
2016 IEEE 55th Conference on Decision and Control, CDC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 6506-6511 7799270 (2016 IEEE 55th Conference on Decision and Control, CDC 2016).

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

Annoni, J, Seiler, P & Jovanovic, MR 2016, Sparsity-promoting dynamic mode decomposition for systems with inputs. in 2016 IEEE 55th Conference on Decision and Control, CDC 2016., 7799270, 2016 IEEE 55th Conference on Decision and Control, CDC 2016, Institute of Electrical and Electronics Engineers Inc., pp. 6506-6511, 55th IEEE Conference on Decision and Control, CDC 2016, Las Vegas, United States, 12/12/16. https://doi.org/10.1109/CDC.2016.7799270

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AB - 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.

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ER -

Sparsity-promoting dynamic mode decomposition for systems with inputs

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