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机器学习在储存环轨道校正中的应用研究

李瑞淳 张庆磊 米清茹 姜伯承 王坤 李昌亮 赵振堂

李瑞淳, 张庆磊, 米清茹, 等. 机器学习在储存环轨道校正中的应用研究[J]. 强激光与粒子束, 2021, 33: 034007. doi: 10.11884/HPLPB202133.200318
引用本文: 李瑞淳, 张庆磊, 米清茹, 等. 机器学习在储存环轨道校正中的应用研究[J]. 强激光与粒子束, 2021, 33: 034007. doi: 10.11884/HPLPB202133.200318
Li Ruichun, Zhang Qinglei, Mi Qingru, et al. Application of machine learning in orbital correction of storage ring[J]. High Power Laser and Particle Beams, 2021, 33: 034007. doi: 10.11884/HPLPB202133.200318
Citation: Li Ruichun, Zhang Qinglei, Mi Qingru, et al. Application of machine learning in orbital correction of storage ring[J]. High Power Laser and Particle Beams, 2021, 33: 034007. doi: 10.11884/HPLPB202133.200318

机器学习在储存环轨道校正中的应用研究

doi: 10.11884/HPLPB202133.200318
基金项目: 国家重点研发计划项目(2016YFA0402001);中国科学院青年创新促进会项目(2020287)
详细信息
    作者简介:

    李瑞淳(1996—),男,硕士研究生,从事储存环轨道校正研究;lirch@shanghaitech.edu.cn

    通讯作者:

    张庆磊(1984—),男,博士,副研究员,从事加速器束流动力学研究;zhangqinglei@zjlab.org.cn

  • 中图分类号: TL54+4

Application of machine learning in orbital correction of storage ring

  • 摘要: X射线同步辐射光源,是现代科学研究中最强大的工具之一。位于中国上海的上海光源,是一台能量为3.5 GeV的先进的第三代中能同步辐射光源。第三代同步辐射光源要提供高亮度、高稳定性的同步辐射来满足实验条件要求苛刻的前沿研究,因此对束流的轨道稳定性有很高的要求。为此,采用机器学习算法进行电子束轨道的控制和反馈。这种基于神经网络的轨道校正方法不依赖于具体的响应矩阵,建立非线性映射关系,并且还可以进行连续的在线再训练,对上海光源的轨道校正和提高束流轨道稳定性有重要意义。
  • 图  1  神经网络原理示意图(x代表输入信号,o代表输出信号)

    Figure  1.  Schematic of neural network (x represents the input signal,o represents the output signal)

    图  2  训练数据与测试数据的绝对平均误差

    Figure  2.  Mean absolute errors of training data and test data

    图  3  训练数据与测试数据的损失函数

    Figure  3.  Loss function of training data and test data

    图  4  机器学习程序的轨道反馈效果。(图中不同颜色曲线代表了不同的BPM数据,第一次扰动对应C01VCM2,第二次扰动对应C04VCM2)

    Figure  4.  Orbit feedback effect of machine learning program. (The different color curves in the figure represent different BPM data. The first disturbance corresponds to corrector C01VCM2 and the second disturbance corresponds to corrector C04VCM2)

    图  5  机器学习程序运行过程中校正铁的变化(图中不同颜色曲线代表了不同的校正铁电流数据,第一次扰动对应C01VCM2,第二次扰动对应C04VCM2)

    Figure  5.  Changes in correctors during the operation of machine learning program.(The different color curves in the figure represent different correctors data. The first disturbance corresponds to C01VCM2 and the second disturbance corresponds to C04VCM2)

    图  6  慢反馈程序的轨道反馈效果

    Figure  6.  Orbit feedback effect of slow feedback program

    图  7  慢反馈程序运行过程中校正铁的变化

    Figure  7.  Changes in correctors during the operation of slow feedback program

    图  8  机器学习程序的轨道反馈效果

    Figure  8.  Orbit feedback effect of machine learning program (corretor current loaded with random disturbance)

    图  9  机器学习程序运行过程中校正铁的变化

    Figure  9.  Changes in correctors during the operation of machine learning program (corretor current loaded with random disturbance)

    图  10  慢反馈程序的轨道反馈效果

    Figure  10.  Orbit feedback effect of slow feedback program (corretor current loaded with random disturbance)

    图  11  慢反馈程序运行过程中校正铁的变化

    Figure  11.  Changes in correctors during the operation of slow feedback program (corretor current loaded with random disturbance)

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出版历程
  • 收稿日期:  2020-11-23
  • 修回日期:  2021-01-19
  • 网络出版日期:  2021-03-30
  • 刊出日期:  2021-03-05

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