Data-driven feedback optimization for particle accelerator application

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.

Subjects

feedback optimization | particle accelerators | recursive least-squares estimation | steady-state control

DDC Class

629.8: Control and Feedback Control Systems

620: Engineering

519: Applied Mathematics, Probabilities

Publication version

publishedVersion

Lizenz

https://creativecommons.org/licenses/by/4.0/

Name

10.1515_auto-2024-0170.pdf

Size

1.29 MB

Format

Adobe PDF

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Data-driven feedback optimization for particle accelerator application