Bias correction and instrumental variables for direct data-driven model-reference control
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915204030267392 |
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| author | Mejari, Manas Breschi, Valentina Formentin, Simone Piga, Dario |
| author_facet | Mejari, Manas Breschi, Valentina Formentin, Simone Piga, Dario |
| contents | Managing noisy data is a central challenge in direct data-driven control design. We propose an approach for synthesizing model-reference controllers for linear time-invariant (LTI) systems using noisy state-input data, employing novel noise mitigation techniques. Specifically, we demonstrate that using data-based covariance parameterization of the controller enables bias-correction and instrumental variable techniques within the data-driven optimization, thus reducing measurement noise effects as data volume increases. The number of decision variables remains independent of dataset size, making this method scalable to large datasets. The approach's effectiveness is demonstrated with a numerical example. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05740 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bias correction and instrumental variables for direct data-driven model-reference control Mejari, Manas Breschi, Valentina Formentin, Simone Piga, Dario Systems and Control Managing noisy data is a central challenge in direct data-driven control design. We propose an approach for synthesizing model-reference controllers for linear time-invariant (LTI) systems using noisy state-input data, employing novel noise mitigation techniques. Specifically, we demonstrate that using data-based covariance parameterization of the controller enables bias-correction and instrumental variable techniques within the data-driven optimization, thus reducing measurement noise effects as data volume increases. The number of decision variables remains independent of dataset size, making this method scalable to large datasets. The approach's effectiveness is demonstrated with a numerical example. |
| title | Bias correction and instrumental variables for direct data-driven model-reference control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2411.05740 |