JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
TY - CONF AU - Diaz Cruz, J.A. AU - Biedron, S. AU - Pirayesh, R. AU - Sosa, S. ED - Zimmermann, Frank ED - Tanaka, Hitoshi ED - Sudmuang, Porntip ED - Klysubun, Prapong ED - Sunwong, Prapaiwan ED - Chanwattana, Thakonwat ED - Petit-Jean-Genaz, Christine ED - Schaa, Volker R.W. TI - Machine Learning-Based Tuning of Control Parameters for LLRF System of Superconducting Cavities J2 - Proc. of IPAC2022, Bangkok, Thailand, 12-17 June 2022 CY - Bangkok, Thailand T2 - International Particle Accelerator Conference T3 - 13 LA - english AB - The multiple systems involved in the operation of particle accelerators use diverse control systems to reach the desired operating point for the machine. Each system needs to tune several control parameters to achieve the required performance. Traditional Low-Level RF (LLRF) systems are implemented as proportional-integral feedback loops, whose gains need to be optimized. In this paper, we explore Machine Learning (ML) as a tool to improve a traditional LLRF controller by tuning its gains using a Neural Network (NN). We present the data production scheme and a control parameter optimization using a NN. The NN training is performed using the THETA supercomputer. PB - JACoW Publishing CP - Geneva, Switzerland SP - 915 EP - 917 KW - cavity KW - controls KW - LLRF KW - simulation KW - SRF DA - 2022/07 PY - 2022 SN - 2673-5490 SN - 978-3-95450-227-1 DO - doi:10.18429/JACoW-IPAC2022-TUPOST027 UR - https://jacow.org/ipac2022/papers/tupost027.pdf ER -