Data-driven model order reduction of linear switched systems

TL;DR

This paper extends the Loewner framework to linear switched systems, enabling data-driven model reduction directly from frequency domain input-output samples without needing the original system matrices.

Contribution

It introduces a novel data-driven model reduction method for linear switched systems using the Loewner framework, handling mode transitions via coupling matrices.

Findings

Enables direct derivation of reduced models from frequency data

Handles mode switching with coupling matrices

Balances accuracy and complexity in reduced models

Abstract

The Loewner framework for model reduction is extended to the class of linear switched systems. One advantage of this framework is that it introduces a trade-off between accuracy and complexity. Moreover, through this procedure, one can derive state-space models directly from data which is related to the input-output behavior of the original system. Hence, another advantage of the framework is that it does not require the initial system matrices. More exactly, the data used in this framework consists in frequency domain samples of input-output mappings of the original system. The definition of generalized transfer functions for linear switched systems resembles the one for bilinear systems. A key role is played by the coupling matrices, which ensure the transition from one active mode to another.