Sequential feedback optimization with application to wind farm control
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915401412116480 |
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| author | Huang, Shijie Grammatico, Sergio |
| author_facet | Huang, Shijie Grammatico, Sergio |
| contents | This paper develops a sequential-linearization feedback optimization framework for driving nonlinear dynamical systems to an
optimal steady state. A fundamental challenge in feedback optimization is the requirement of accurate first-order information
of the steady-state input-output mapping, which is computationally prohibitive for high-dimensional nonlinear systems and
often leads to poor performance when approximated around a fixed operating point. To address this limitation, we propose a
sequential algorithm that adaptively updates the linearization point during optimization, maintaining local accuracy throughout
the trajectory. We prove convergence to a neighborhood of the optimal steady state with explicit error bounds. To reduce the
computational burden of repeated linearization operations, we further develop a multi-timescale variant where linearization
updates occur at a slower timescale than optimization iterations, achieving significant computational savings while preserving
convergence guarantees. The effectiveness of the proposed framework is demonstrated via numerical simulations of a realistic
wind farm control problem. The results validate both the theoretical convergence predictions and the expected computational
advantages of our multi-timescale formulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15127 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sequential feedback optimization with application to wind farm control Huang, Shijie Grammatico, Sergio Optimization and Control Systems and Control This paper develops a sequential-linearization feedback optimization framework for driving nonlinear dynamical systems to an optimal steady state. A fundamental challenge in feedback optimization is the requirement of accurate first-order information of the steady-state input-output mapping, which is computationally prohibitive for high-dimensional nonlinear systems and often leads to poor performance when approximated around a fixed operating point. To address this limitation, we propose a sequential algorithm that adaptively updates the linearization point during optimization, maintaining local accuracy throughout the trajectory. We prove convergence to a neighborhood of the optimal steady state with explicit error bounds. To reduce the computational burden of repeated linearization operations, we further develop a multi-timescale variant where linearization updates occur at a slower timescale than optimization iterations, achieving significant computational savings while preserving convergence guarantees. The effectiveness of the proposed framework is demonstrated via numerical simulations of a realistic wind farm control problem. The results validate both the theoretical convergence predictions and the expected computational advantages of our multi-timescale formulation. |
| title | Sequential feedback optimization with application to wind farm control |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2507.15127 |