1–6 Jun 2025
Taipei International Convention Center (TICC)
Asia/Taipei timezone

Efficient data-driven model predictive control for online accelerator tuning

THPM116
5 Jun 2025, 15:30
2h
Exhibiton Hall A _Magpie (TWTC)

Exhibiton Hall A _Magpie

TWTC

Poster Presentation MC6.D13 Machine Learning Thursday Poster Session

Speaker

Andrea Santamaria Garcia (Karlsruhe Institute of Technology)

Description

Reinforcement learning (RL) is a promising approach for the online control of complex, real-world systems, with recent success demonstrated in applications such as particle accelerator control. However, model-free RL algorithms often suffer from sample inefficiency, making training infeasible without access to high-fidelity simulations or extensive measurement data. This limitation poses a significant challenge for efficient real-world deployment. In this work, we explore data-driven model-predictive control (MPC) as a solution. Specifically, we employ Gaussian processes (GPs) to model the unknown transition functions in the real-world system, enabling safe exploration in the training process. We apply the GP-MPC framework to the transverse beam tuning task at the ARES accelerator, demonstrating its potential for efficient online training. This study showcases the feasibility of data-driven control strategies for accelerator applications, paving the way for more efficient and effective solutions in real-world scenarios.

Region represented Europe
Paper preparation format LaTeX

Author

Chenran Xu (Karlsruhe Institute of Technology)

Co-authors

Andrea Santamaria Garcia (Karlsruhe Institute of Technology) Jan Kaiser (Deutsches Elektronen-Synchrotron DESY) Christian Hespe (Deutsches Elektronen-Synchrotron DESY) Annika Eichler (Deutsches Elektronen-Synchrotron DESY) Borja Rodriguez Mateos (European Organization for Nuclear Research) Simon Hirlaender (University of Salzburg)

Presentation materials

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