论文标题
SSRF存储环的基于机器学习的封闭轨道反馈
A machine-learning based closed orbit feedback for the SSRF storage ring
论文作者
论文摘要
为了提高同步辐射的稳定性，我们开发了一种基于机器学习的封闭轨道反馈的新方法，并将其在上海同步辐射设施（SSRF）的存储环中进行了试点。在我们的实验中，基于机器学习的封闭轨道反馈不仅可以同时进行水平，垂直和RF频率反馈，而且比传统的慢速轨道反馈（SOFB）系统具有更好的收敛性和收敛速度。此外，校正后电流变化的残差值几乎可以忽略。这种基于机器学习的新方法有望建立新的封闭轨道反馈系统，并在日常操作中提高存储环的轨道稳定性。
In order to improve the stability of synchrotron radiation, we developed a new method of machine learning-based closed orbit feedback and piloted it in the storage ring of the Shanghai Synchrotron Radiation Facility (SSRF). In our experiments, not only can the machine learning-based closed orbit feedback carry out horizontal, vertical and RF frequency feedback simultaneously, but it also has better convergence and convergence speed than the traditional Slow Orbit Feed Back (SOFB) system. What's more, the residual values of the correctors' currents variations after correction can be almost ignored. This machine learning-based new method is expected to establish a new closed orbit feedback system and improve the orbit stability of the storage ring in daily operation.
