TY  - JOUR
AU  - Hespe, Christian
AU  - Kaiser, Jan
AU  - Luebsen, Jannis
AU  - Mayet, Frank
AU  - Scholz, Matthias
AU  - Eichler, Annika
TI  - Data-Driven Feedback Optimization for Particle Accelerator Application
JO  - Automatisierungstechnik
VL  - 73
IS  - 6
SN  - 0178-2312
CY  - Berlin
PB  - De Gruyter
M1  - PUBDB-2024-07329
SP  - 429 - 440
PY  - 2025
N1  - EuXFEL R&D project “RP-513: Learning Based Methods”
AB  - 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.
LB  - PUB:(DE-HGF)16
DO  - DOI:10.1515/auto-2024-0170
UR  - https://bib-pubdb1.desy.de/record/619029
ER  -