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| URL | https://doi.org/10.18429/JACoW-IPAC2025-THPM116 |
|---|---|
| Title | Efficient data-driven model predictive control for online accelerator tuning |
| Authors |
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| Abstract | 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. |
| Paper | download: THPM116.pdf |
| Cite | BibTeX, LaTeX, Text/Word, RIS, EndNote |
| Conference | 16th International Particle Accelerator Conference |
| Series | |
| Location | Taipei, Taiwan |
| Date | 01-06 Jun 2025 |
| Publisher | JACoW Publishing, Geneva, Switzerland |
| Editorial Board | * Ming-Chyuan Lin - National Synchrotron Radiation Research Center * Yoichi Sato - KEK * Jui-Che Huang - National Synchrotron Radiation Research Center * David Button - Australian Nuclear Science and Technology Organisation * Ping-Shun Chuang - National Synchrotron Radiation Research Center |
| Online ISBN | 978-3-95450-248-6 |
| Online ISSN | 2673-5490 |
| Received | 27 May 2025 |
| Revised | 02 June 2025 |
| Accepted | 04 June 2025 |
| Issued | 05 November 2025 |
| DOI | 10.18429/JACoW-IPAC2025-THPM116 |
| Pages | 2931-2934 |
| Copyright | Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |