Identifying Large-Scale Linear Parameter Varying Systems with Dynamic Mode Decomposition Methods

• URL: http://arxiv.org/abs/2502.02336v1
• Date: Tue, 04 Feb 2025 14:15:16 GMT
• Title: Identifying Large-Scale Linear Parameter Varying Systems with Dynamic Mode Decomposition Methods
• Authors: Jean Panaioti Jordanou, Eduardo Camponogara, Eduardo Gildin,
• Abstract summary: This work develops a methodology for the local and global identification of large-scale LPV systems. The method is coined as DMD-LPV for being inspired in the Dynamic Mode Decomposition (DMD) Experiments show that the proposed method can easily identify a reduced-order model of a given large-scale system without the need to perform identification in the full-order dimension.
• Score: 5.217516787417401
• License:
• Abstract: Linear Parameter Varying (LPV) Systems are a well-established class of nonlinear systems with a rich theory for stability analysis, control, and analytical response finding, among other aspects. Although there are works on data-driven identification of such systems, the literature is quite scarce in terms of works that tackle the identification of LPV models for large-scale systems. Since large-scale systems are ubiquitous in practice, this work develops a methodology for the local and global identification of large-scale LPV systems based on nonintrusive reduced-order modeling. The developed method is coined as DMD-LPV for being inspired in the Dynamic Mode Decomposition (DMD). To validate the proposed identification method, we identify a system described by a discretized linear diffusion equation, with the diffusion gain defined by a polynomial over a parameter. The experiments show that the proposed method can easily identify a reduced-order LPV model of a given large-scale system without the need to perform identification in the full-order dimension, and with almost no performance decay over performing a reduction, given that the model structure is well-established.

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