Identification of additive multivariable continuous-time systems
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911029106049024 |
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| author | van der Hulst, Maarten González, Rodrigo Classens, Koen Dirkx, Nic van de Wijdeven, Jeroen Oomen, Tom |
| author_facet | van der Hulst, Maarten González, Rodrigo Classens, Koen Dirkx, Nic van de Wijdeven, Jeroen Oomen, Tom |
| contents | Multivariable parametric models are critical for designing, controlling, and optimizing the performance of engineered systems. The main aim of this paper is to develop a parametric identification strategy that delivers accurate and physically relevant models of multivariable systems using time-domain data. The introduced approach adopts an additive model structure, providing a parsimonious and interpretable representation of many physical systems, and applies a refined instrumental variable-based estimation algorithm. The developed identification method enables the estimation of multivariable parametric additive models in continuous time and is applicable to both open- and closed-loop systems. The performance of the estimator is demonstrated through numerical simulations and experimentally validated on a flexible beam system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01639 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identification of additive multivariable continuous-time systems van der Hulst, Maarten González, Rodrigo Classens, Koen Dirkx, Nic van de Wijdeven, Jeroen Oomen, Tom Signal Processing Multivariable parametric models are critical for designing, controlling, and optimizing the performance of engineered systems. The main aim of this paper is to develop a parametric identification strategy that delivers accurate and physically relevant models of multivariable systems using time-domain data. The introduced approach adopts an additive model structure, providing a parsimonious and interpretable representation of many physical systems, and applies a refined instrumental variable-based estimation algorithm. The developed identification method enables the estimation of multivariable parametric additive models in continuous time and is applicable to both open- and closed-loop systems. The performance of the estimator is demonstrated through numerical simulations and experimentally validated on a flexible beam system. |
| title | Identification of additive multivariable continuous-time systems |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.01639 |