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@ARTICLE{Hespe:619029,
author = {Hespe, Christian and Kaiser, Jan and Luebsen, Jannis and
Mayet, Frank and Scholz, Matthias and Eichler, Annika},
title = {{D}ata-{D}riven {F}eedback {O}ptimization for {P}article
{A}ccelerator {A}pplication},
journal = {Automatisierungstechnik},
volume = {73},
number = {6},
issn = {0178-2312},
address = {Berlin},
publisher = {De Gruyter},
reportid = {PUBDB-2024-07329},
pages = {429 - 440},
year = {2025},
note = {EuXFEL $R\&D$ project “RP-513: Learning Based Methods”},
abstract = {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.},
cin = {MSK / MXL},
ddc = {620},
cid = {I:(DE-H253)MSK-20120731 / I:(DE-H253)MXL-20160301},
pnm = {621 - Accelerator Research and Development (POF4-621)},
pid = {G:(DE-HGF)POF4-621},
experiment = {EXP:(DE-H253)XFEL(machine)-20150101},
typ = {PUB:(DE-HGF)16},
doi = {10.1515/auto-2024-0170},
url = {https://bib-pubdb1.desy.de/record/619029},
}