Data-driven Structured Realization
Philipp Schulze, Benjamin Unger, Christopher Beattie, Serkan Gugercin

TL;DR
This paper introduces a data-driven framework for constructing structured linear dynamical system models from transfer function measurements, extending the Loewner realization method to more complex system structures including delays and higher-order systems.
Contribution
It extends the Loewner realization framework to handle structured systems with prescribed functions, enabling data-driven modeling of complex dynamical systems.
Findings
Successfully constructs structured realizations from transfer function data.
Applicable to second-order, delay, and higher-order systems.
Numerical examples demonstrate effectiveness and advantages.
Abstract
We present a framework for constructing structured realizations of linear dynamical systems having transfer functions of the form where are prescribed functions that specify the surmised structure of the model. Our construction is data-driven in the sense that an interpolant is derived entirely from measurements of a transfer function. Our approach extends the Loewner realization framework to more general system structure that includes second-order (and higher) systems as well as systems with internal delays. Numerical examples demonstrate the advantages of this approach.
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