Model Reduction for Fractional-Order Port-Hamiltonian Systems in the Loewner Framework

This paper addresses the problem of model order reduction for fractional-order port-Hamiltonian systems (FoPHS) within the Loewner framework. Building on the classical Loewner approach, the study extended the framework to incorporate fractional-order dynamics while preserving the port-Hamiltonian structure in reduced-order models. Specifically, the method was adapted to fractional-order systems by employing right- and left-tangential interpolation data, deriving conditions for constructing an interpolant that retains both the fractional-order behavior and the energy-conserving structure. Additionally, a novel interpolation data selection method was proposed, and numerical simulations validated the reduction approach, demonstrating its effectiveness and accuracy in preserving the system's inherent properties.

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Publication Details

Published
2025-07-04
DOI
https://doi.org/10.1109/fasta65681.2025.11138172
Primary Topic
Model Reduction and Neural Networks
Type
article
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Model Reduction for Fractional-Order Port-Hamiltonian Systems in the Loewner Framework

Jinhua Zhang, Yiheng Wei, Zixi Guan, Rui Chen
Model Reduction and Neural Networks
article

Model Reduction for Fractional-Order Port-Hamiltonian Systems in the Loewner Framework

Jinhua Zhang, Yiheng Wei, Zixi Guan, Rui Chen
article en

Abstract

This paper addresses the problem of model order reduction for fractional-order port-Hamiltonian systems (FoPHS) within the Loewner framework. Building on the classical Loewner approach, the study extended the framework to incorporate fractional-order dynamics while preserving the port-Hamiltonian structure in reduced-order models. Specifically, the method was adapted to fractional-order systems by employing right- and left-tangential interpolation data, deriving conditions for constructing an interpolant that retains both the fractional-order behavior and the energy-conserving structure. Additionally, a novel interpolation data selection method was proposed, and numerical simulations validated the reduction approach, demonstrating its effectiveness and accuracy in preserving the system's inherent properties.

Southeast University (CN)
Openalex Percentile: Top 81%
Model Reduction and Neural Networks
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