Controller Synthesis from Noisy-Input Noisy-Output Data
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909790803853312 |
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| author | Li, Lidong Bisoffi, Andrea De Persis, Claudio Monshizadeh, Nima |
| author_facet | Li, Lidong Bisoffi, Andrea De Persis, Claudio Monshizadeh, Nima |
| contents | We consider the problem of synthesizing a dynamic output-feedback controller for a linear system, using solely input-output data corrupted by measurement noise. To handle input-output data, an auxiliary representation of the original system is introduced. By exploiting the structure of the auxiliary system, we design a controller that robustly stabilizes all possible systems consistent with data. Notably, we also provide a novel solution to extend the results to generic multi-input multi-output systems. The findings are illustrated by numerical examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02588 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Controller Synthesis from Noisy-Input Noisy-Output Data Li, Lidong Bisoffi, Andrea De Persis, Claudio Monshizadeh, Nima Systems and Control Machine Learning We consider the problem of synthesizing a dynamic output-feedback controller for a linear system, using solely input-output data corrupted by measurement noise. To handle input-output data, an auxiliary representation of the original system is introduced. By exploiting the structure of the auxiliary system, we design a controller that robustly stabilizes all possible systems consistent with data. Notably, we also provide a novel solution to extend the results to generic multi-input multi-output systems. The findings are illustrated by numerical examples. |
| title | Controller Synthesis from Noisy-Input Noisy-Output Data |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2402.02588 |