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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    Title :

    Data-driven feedback optimization for particle accelerator application


    Additional title:

    Datengestützte Feedbackoptimierung für die Anwendung an Teilchenbeschleunigern


    Contributors:

    Published in:

    Publication date :

    2025




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629 / 519 / 620





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