Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear Systems
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914802971967488 |
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| author | Koelewijn, Patrick J. W. Sing, Rajiv Seiler, Peter Tóth, Roland |
| author_facet | Koelewijn, Patrick J. W. Sing, Rajiv Seiler, Peter Tóth, Roland |
| contents | In this paper, we consider the learning of a Reduced-Order Linear Parameter-Varying Model (ROLPVM) of a nonlinear dynamical system based on data. This is achieved by a two-step procedure. In the first step, we learn a projection to a lower dimensional state-space. In step two, an LPV model is learned on the reduced-order state-space using a novel, efficient parameterization in terms of neural networks. The improved modeling accuracy of the method compared to an existing method is demonstrated by simulation examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_06217 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear Systems Koelewijn, Patrick J. W. Sing, Rajiv Seiler, Peter Tóth, Roland Systems and Control In this paper, we consider the learning of a Reduced-Order Linear Parameter-Varying Model (ROLPVM) of a nonlinear dynamical system based on data. This is achieved by a two-step procedure. In the first step, we learn a projection to a lower dimensional state-space. In step two, an LPV model is learned on the reduced-order state-space using a novel, efficient parameterization in terms of neural networks. The improved modeling accuracy of the method compared to an existing method is demonstrated by simulation examples. |
| title | Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear Systems |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2312.06217 |