Contribution to a conference proceedings

GSI-2025-00536

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Data-driven model predictive control for automated optimization of injection into the SIS18 synchrotron

Appel, S. (Corresponding author)GSI* ; Hirlaender, S. ; Madysa, N.GSI* ; Pilat, F. (Editor) ; Fischer, W. (Editor) ; Saethre, R. (Editor) ; Anisimov, P. (Editor) ; Andrian, I. (Editor)

2024
JACoW Publishing Geneva, Switzerland

Please use a persistent id in citations: doi:10.18429/JACOW-IPAC2024-TUPS59  doi:10.15120/GSI-2025-00536

Abstract: In accelerator labs like GSI/FAIR, automating complex systems is key for maximizing physics experiment time. This study explores the application of a data-driven model predictive control (MPC) to refine the multi-turn injection (MTI) process into the SIS18 synchrotron, departing from conventional numerical optimization methods. MPC is distinguished by its reduced number of optimization steps and superior ability to control performance criteria, effectively addressing issues like delayed outcomes and safety concerns, including septum protection. The study focuses on a highly sample-efficient MPC approach based on Gaussian processes, which lies at the intersection of model-based reinforcement learning and control theory. This approach merges the strengths of both fields, offering a unified and optimized solution and yielding a safe and fast state-based optimization approach beyond classical reinforcement learning and Bayesian optimization. Our study lays the groundwork for enabling safe online training for the SS18 MTI issue, showing great potential for applying data-driven control in similar scenarios.

Keyword(s): Accelerator Physics ; mc6-beam-instrumentation-controls-feedback-and-operational-aspects - MC6: Beam Instrumentation, Controls, Feedback, and Operational Aspects ; MC6.D13 - MC6.D13 Machine Learning

Note: Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 license.

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Contributing Institute(s):

1. Accelerator Physics (APH)

Research Program(s):

1. 899 - ohne Topic (POF4-899) (POF4-899)

Experiment(s):

1. no experiment theory work (theory)

Appears in the scientific report 2025

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