The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
TY - CONF AU - Pirayesh, R. AU - Biedron, S. AU - Diaz Cruz, J.A. AU - Martinez-Ramon, M. AU - Sosa Guitron, S.I. ED - White, Karen S. ED - Brown, Kevin A. ED - Dyer, Philip S. ED - Schaa, Volker RW TI - Achieving Optimal Control of LLRF Control System with Artificial Intelligence J2 - Proc. of ICALEPCS2019, New York, NY, USA, 05-11 October 2019 CY - New York, NY, USA T2 - International Conference on Accelerator and Large Experimental Physics Control Systems T3 - 17 LA - english AB - Artificial Intelligence is a versatile tool to make machines learn the characteristics of a device or a system. In this research, we will be investigating applying deep learning and Gaussian process learning to make a machine learn the optimal settings of a low-level RF (LLRF) control system for particle accelerators. These settings include the multiple controllers’ parameters and the parameters of the LLRF that result in an optimal target function applied to the LLRF. Finding this target function, finding the right machine learning algorithm with the lowest error, and finding the best controller that result in the most optimal target function is the goal of this research. PB - JACoW Publishing CP - Geneva, Switzerland SP - 488 EP - 492 KW - controls KW - cavity KW - LLRF KW - SRF KW - framework DA - 2020/08 PY - 2020 SN - 2226-0358 SN - 978-3-95450-209-7 DO - doi:10.18429/JACoW-ICALEPCS2019-MOPHA114 UR - https://jacow.org/icalepcs2019/papers/mopha114.pdf ER -