On Reduced Input-Output Dynamic Mode Decomposition

TL;DR

This paper explores input-output dynamic mode decomposition for system identification, comparing excitation methods and proposing an optimization-based stabilization to improve the generalization of reduced-order models.

Contribution

It introduces an optimization-based stabilization strategy and compares excitation approaches for more reliable input-output system identification.

Findings

Optimization-based stabilization improves model robustness.

Different excitation methods impact identification accuracy.

Enhanced reduced-order models better approximate system behavior.

Abstract

The identification of reduced-order models from high-dimensional data is a challenging task, and even more so if the identified system should not only be suitable for a certain data set, but generally approximate the input-output behavior of the data source. In this work, we consider the input-output dynamic mode decomposition method for system identification. We compare excitation approaches for the data-driven identification process and describe an optimization-based stabilization strategy for the identified systems.