Leveraging Non-Steady-State Frequency-Domain Data in Willems' Fundamental Lemma
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910906945896448 |
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| author | Meijer, T. J. Wind, M. Dolk, V. S. Heemels, W. P. M. H. |
| author_facet | Meijer, T. J. Wind, M. Dolk, V. S. Heemels, W. P. M. H. |
| contents | Willems' fundamental lemma enables data-driven analysis and control by characterizing an unknown system's behavior directly in terms of measured data. In this work, we extend a recent frequency-domain variant of this result--previously limited to steady-state data--to incorporate non-steady-state data including transient phenomena. This approach eliminates the need to wait for transients to decay during data collection, significantly reducing the experiment duration. Unlike existing frequency-domain system identification methods, our approach integrates transient data without preprocessing, making it well-suited for direct data-driven analysis and control. We demonstrate its effectiveness by isolating transients in the collected data and performing FRF evaluation at arbitrary frequencies in a numerical case study including noise. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06403 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging Non-Steady-State Frequency-Domain Data in Willems' Fundamental Lemma Meijer, T. J. Wind, M. Dolk, V. S. Heemels, W. P. M. H. Systems and Control Optimization and Control Willems' fundamental lemma enables data-driven analysis and control by characterizing an unknown system's behavior directly in terms of measured data. In this work, we extend a recent frequency-domain variant of this result--previously limited to steady-state data--to incorporate non-steady-state data including transient phenomena. This approach eliminates the need to wait for transients to decay during data collection, significantly reducing the experiment duration. Unlike existing frequency-domain system identification methods, our approach integrates transient data without preprocessing, making it well-suited for direct data-driven analysis and control. We demonstrate its effectiveness by isolating transients in the collected data and performing FRF evaluation at arbitrary frequencies in a numerical case study including noise. |
| title | Leveraging Non-Steady-State Frequency-Domain Data in Willems' Fundamental Lemma |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2504.06403 |