Work

Structure-Preserving Model Reduction of Negative Imaginary Systems

Year: 2026

Type: conference-paper

Abstract: This paper presents a structure-preserving model reduction framework for negative imaginary (NI) systems based on a state-space parametrisation. We first establish a constructive representation showing that any NI system without poles at the origin can be parametrised by a skew-symmetric, a positive semi-definite, an arbitrary, and a symmetric matrix, thereby revealing the internal state-space structure underlying the NI property. Leveraging this parametrisation, a projection-based model reduction method is developed that guarantees both the preservation of the NI property and exact interpolation of the transfer function at prescribed frequency points. Numerical examples on flexible structures are presented to demonstrate the validity of the proposed parametrisation, the interpolation accuracy and structure-preserving capability of the proposed method.

Source: Research Explorer (The University of Manchester)

Authors: Liu, Chang, Su, Lanlan; id_orcid 0000-0002-6489-3253, Lanzon, Alexander; id_orcid 0000-0003-3424-8808

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7155574924

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