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100 1 _ |a Hespe, Christian
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245 _ _ |a Data-Driven Feedback Optimization for Particle Accelerator Application
260 _ _ |a Berlin
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500 _ _ |a EuXFEL R&D project “RP-513: Learning Based Methods”
520 _ _ |a For many engineering problems involving control systems, finding a good working point for steady-state operation is crucial. Therefore, this paper presents an application of steady-state optimization with feedback on particle accelerators, specifically the European X-ray free-electron laser. In simulation studies, we demonstrate that feedback optimization is able to reach a near-optimal steady-state operation in the presence of uncertainties, even without relying on a priori known model information but purely data-driven through input-output measurements. Additionally, we discuss the importance of including second-order information in the optimization to ensure a satisfactory convergence speed and propose an approximated Hessian representation for problems without second-order knowledge on the plant.
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700 1 _ |a Kaiser, Jan
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700 1 _ |a Luebsen, Jannis
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700 1 _ |a Mayet, Frank
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700 1 _ |a Scholz, Matthias
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700 1 _ |a Eichler, Annika
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770 _ _ |a Data-Driven and Learning-Based Control – Perspectives and Prospects
773 _ _ |a 10.1515/auto-2024-0170
|g Vol. 73, no. 6, p. 429 - 440
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|t Automatisierungstechnik
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