Identification of diffusively coupled linear networks through structured polynomial models
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2021
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913037381795840 |
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| author | M., E. M. Kivits Hof, Paul M. J. Van den |
| author_facet | M., E. M. Kivits Hof, Paul M. J. Van den |
| contents | Physical dynamic networks most commonly consist of interconnections of physical components that can be described by diffusive couplings. These diffusive couplings imply that the cause-effect relationships in the interconnections are symmetric and therefore physical dynamic networks can be represented by undirected graphs. This paper shows how prediction error identification methods developed for linear time-invariant systems in polynomial form can be configured to consistently identify the parameters and the interconnection structure of diffusively coupled networks. Further, a multi-step least squares convex optimization algorithm is developed to solve the nonconvex optimization problem that results from the identification method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2106_01813 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Identification of diffusively coupled linear networks through structured polynomial models M., E. M. Kivits Hof, Paul M. J. Van den Systems and Control Physical dynamic networks most commonly consist of interconnections of physical components that can be described by diffusive couplings. These diffusive couplings imply that the cause-effect relationships in the interconnections are symmetric and therefore physical dynamic networks can be represented by undirected graphs. This paper shows how prediction error identification methods developed for linear time-invariant systems in polynomial form can be configured to consistently identify the parameters and the interconnection structure of diffusively coupled networks. Further, a multi-step least squares convex optimization algorithm is developed to solve the nonconvex optimization problem that results from the identification method. |
| title | Identification of diffusively coupled linear networks through structured polynomial models |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2106.01813 |