Data-Driven Model Reduction by Moment Matching for Linear and Nonlinear Parametric Systems

2025/06/12 by Hanqing Zhang, Zhang, Hanqing, Junyu Mao +5 · 1 citation
Engineering · Physics and Astronomy · #Control Systems and Identification #FOS: Electrical engineering #Hydraulic and Pneumatic Systems #Model Reduction and Neural Networks #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2506.10866

openalex publication_date 2025/06/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Theory and methods to obtain parametric reduced-order models by moment matching are presented. The definition of the parametric moment is introduced, and methods (model-based and data-driven) for the approximation of the parametric moment of linear and nonlinear parametric systems are proposed. These approximations are exploited to construct families of parametric reduced-order models that match the approximate parametric moment of the system to be reduced and preserve key system properties such as asymptotic stability and dissipativity. The use of the model reduction methods is illustrated by means of a parametric benchmark model for the linear case and a large-scale wind farm model for the nonlinear case. In the illustration, a comparison of the proposed approximation methods is drawn and their advantages/disadvantages are discussed.

Citations

• Model Order Reduction of Large-Scale Wind Farms: A Data-Driven Approach
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Cited by

• One Equation to Rule Them All -- Part II: Direct Data-Driven Reduction and Regulation

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