%0 Journal Article
%A Hespe, Christian
%A Kaiser, Jan
%A Luebsen, Jannis
%A Mayet, Frank
%A Scholz, Matthias
%A Eichler, Annika
%T Data-Driven Feedback Optimization for Particle Accelerator Application
%J Automatisierungstechnik
%V 73
%N 6
%@ 0178-2312
%C Berlin
%I De Gruyter
%M PUBDB-2024-07329
%P 429 - 440
%D 2025
%Z EuXFEL R&D project “RP-513: Learning Based Methods”
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%R 10.1515/auto-2024-0170
%U https://bib-pubdb1.desy.de/record/619029