Sparsity-promoting dynamic mode decomposition for systems with inputs

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.