Identifying Large-Scale Linear Parameter Varying Systems with Dynamic Mode Decomposition Methods
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912486863667200 |
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| author | Jordanou, Jean Panaioti Camponogara, Eduardo Gildin, Eduardo |
| author_facet | Jordanou, Jean Panaioti Camponogara, Eduardo Gildin, Eduardo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_02336 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identifying Large-Scale Linear Parameter Varying Systems with Dynamic Mode Decomposition Methods Jordanou, Jean Panaioti Camponogara, Eduardo Gildin, Eduardo Systems and Control Machine Learning 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. |
| title | Identifying Large-Scale Linear Parameter Varying Systems with Dynamic Mode Decomposition Methods |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2502.02336 |