Robust Feedback Optimization with Model Uncertainty: A Regularization Approach
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914025075376128 |
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| author | Chan, Winnie He, Zhiyu Moffat, Keith Bolognani, Saverio Muehlebach, Michael Dörfler, Florian |
| author_facet | Chan, Winnie He, Zhiyu Moffat, Keith Bolognani, Saverio Muehlebach, Michael Dörfler, Florian |
| contents | Feedback optimization optimizes the steady state of a dynamical system by implementing optimization iterations in closed loop with the plant. It relies on online measurements and limited model information, namely, the input-output sensitivity. In practice, various issues including inaccurate modeling, lack of observation, or changing conditions can lead to sensitivity mismatches, causing closed-loop sub-optimality or even instability. To handle such uncertainties, we pursue robust feedback optimization, where we optimize the closed-loop performance against all possible sensitivities lying in specific uncertainty sets. We provide tractable reformulations for the corresponding min-max problems via regularizations and characterize the online closed-loop performance through the tracking error in case of time-varying optimal solutions. Simulations on a distribution grid illustrate the effectiveness of our robust feedback optimization controller in addressing sensitivity mismatches in a non-stationary environment. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_24151 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Feedback Optimization with Model Uncertainty: A Regularization Approach Chan, Winnie He, Zhiyu Moffat, Keith Bolognani, Saverio Muehlebach, Michael Dörfler, Florian Optimization and Control Systems and Control Feedback optimization optimizes the steady state of a dynamical system by implementing optimization iterations in closed loop with the plant. It relies on online measurements and limited model information, namely, the input-output sensitivity. In practice, various issues including inaccurate modeling, lack of observation, or changing conditions can lead to sensitivity mismatches, causing closed-loop sub-optimality or even instability. To handle such uncertainties, we pursue robust feedback optimization, where we optimize the closed-loop performance against all possible sensitivities lying in specific uncertainty sets. We provide tractable reformulations for the corresponding min-max problems via regularizations and characterize the online closed-loop performance through the tracking error in case of time-varying optimal solutions. Simulations on a distribution grid illustrate the effectiveness of our robust feedback optimization controller in addressing sensitivity mismatches in a non-stationary environment. |
| title | Robust Feedback Optimization with Model Uncertainty: A Regularization Approach |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2503.24151 |