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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.15922 |
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| _version_ | 1866908973868777472 |
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| author | Aarnoudse, Leontine Haring, Mark van de Wouw, Nathan Pavlov, Alexey |
| author_facet | Aarnoudse, Leontine Haring, Mark van de Wouw, Nathan Pavlov, Alexey |
| contents | Data-based adaptive optimization methods hold great promise for the performance optimization of uncertain, time-varying processes. However, current methods are often based on continuous perturbation which is in general undesired for real-life (e.g., industrial) applications. In this paper, a new uncertainty-based perturb-and-observe method is developed that addresses this limitation and reduces the required number of perturbations, while retaining the capability to track time-varying optima. The method is based on the philosophy of `only perturbing when needed,' and is shown to converge to the optimum under mild conditions. A simulation-based case study on a photo-voltaic solar array demonstrates that it can outperform the standard perturb and observe approach as well as three other data-based optimization methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_15922 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Uncertainty-based perturb and observe for data-driven optimization Aarnoudse, Leontine Haring, Mark van de Wouw, Nathan Pavlov, Alexey Systems and Control Data-based adaptive optimization methods hold great promise for the performance optimization of uncertain, time-varying processes. However, current methods are often based on continuous perturbation which is in general undesired for real-life (e.g., industrial) applications. In this paper, a new uncertainty-based perturb-and-observe method is developed that addresses this limitation and reduces the required number of perturbations, while retaining the capability to track time-varying optima. The method is based on the philosophy of `only perturbing when needed,' and is shown to converge to the optimum under mild conditions. A simulation-based case study on a photo-voltaic solar array demonstrates that it can outperform the standard perturb and observe approach as well as three other data-based optimization methods. |
| title | Uncertainty-based perturb and observe for data-driven optimization |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2604.15922 |