Machine learning for online control of particle accelerators  

作　　者：Xiaolong Chen Zhijun Wang Yuan He Hong Zhao Chunguang Su Shuhui Liu Weilong Chen Xiaoying Zhao Xin Qi Kunxiang Sun Chao Jin Yimeng Chu Hongwei Zhao 

机构地区：[1]Institute of Modern Physics,Chinese Academy of Sciences,Lanzhou 730000,China [2]School of Nuclear Science and Technology,Lanzhou University,Lanzhou 730000,China [3]School of Nuclear Science and Technology,University of Chinese Academy of Sciences,Beijing 100049,China [4]Advanced Energy Science and Technonlogy Guangdong Laboratory,Huizhou 516000,China [5]Department of Physics,Xiamen University,Xiamen 361005,China

出　　处：《Science China(Physics,Mechanics & Astronomy)》2025年第2期94-104,共11页中国科学:物理学、力学、天文学(英文版)

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.12405189,12475161,11525523,and U1730122);the Large Research Infrastructures China Initiative Accelerator Driven System(Grant No.2017-000052-75-01-000590)。

摘　　要：Particle accelerators play a critical role in modern scientific research.However,existing manual beam control methods heavily rely on experienced operators,leading to significant time consumption and potential challenges in managing next-generation accelerators characterized by higher beam current and stronger nonlinear properties.In this paper,we establish a dynamical foundation for designing the online adaptive controller of accelerators using machine learning.This provides a guarantee for dynamic controllability for a class of scientific instruments whose dynamics are described by spatial-temporal equations of motion but only part variables along the instruments under steady states are available.The necessity of using historical time series of beam diagnostic data is emphasised.Key strategies involve also employing a well-established virtual beamline of accelerators,by which various beam calibration scenarios that actual accelerators may encounter are produced.Then the reinforcement learning algorithm is adopted to train the controller with the interaction to the virtual beamline.Finally,the controller is seamlessly transitioned to real ion accelerators,enabling efficient online adaptive control and maintenance.Notably,the controller demonstrates significant robustness,effectively managing beams with diverse charge mass ratios without requiring retraining.Such a controller allows us to achieve the global control within the entire superconducting section of the China Accelerator Facility for Superheavy Elements.

关 键 词：nonlinear complex system adaptive control machine learning particle accelerator simulation to reality 

分 类 号：TL50[核科学技术—核技术及应用] TP181[自动化与计算机技术—控制理论与控制工程]