Application of ensemble machine learning algorithms and filtering techniques in slow orbit feedback systems of electron storage rings
Year: 2025
Type: article
Abstract: Orbit stability is a critical performance metric for modern synchrotron radiation facilities and colliders, necessitating effective orbit correction and slow orbit feedback systems. In recent years, innovative orbit feedback methods leveraging neural networks have been progressively implemented, demonstrating improved performance. BEPCII has also attempted to implement an orbit feedback system based on neural networks. Furthermore, we have noted that orbit stability is significantly affected by noises. To address the impact of noises, we introduced the Kalman filtering method into the orbit feedback system and combined it with the existing neural network framework, creating a dual neural network orbit feedback system. This innovative approach has led to further improvements in overall orbit stability.
Authors: Jiaqi Fan, Weibin Liu, Jiuqing Wang, Yanru Wei, Yuanyuan Wei, Daheng Ji
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Funder: Institute of High Energy Physics
OpenAlex ID: W4409159988