The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
TY - CONF AU - Diaz Cruz, J.A. AU - Biedron, S. AU - Martinez-Ramon, M. AU - Pirayesh, R. AU - Sosa Guitron, S.I. ED - Yamazaki, Yoshishige ED - Raubenheimer, Tor ED - McCausey, Amy ED - Schaa, Volker RW TI - Studies in Applying Machine Learning to Resonance Control in Superconducting RF Cavities J2 - Proc. of NAPAC2019, Lansing, MI, USA, 01-06 September 2019 CY - Lansing, MI, USA T2 - North American Particle Accelerator Conference T3 - 4 LA - english AB - Traditional PID, active resonance and feed-forward controllers are dominant strategies for cavity resonance control, but performance may be limited for systems with tight detuning requirements, as low as 10 Hz peak detuning (few nanometers change in cavity length), that are affected by microphonics and Lorentz Force Detuning. Microphonic sources depend on cavity and cryomodule mechanical couplings with their environment and come from several systems: cryoplant, RF sources, tuners, etc. A promising avenue to overcome the limitations of traditional resonance control techniques is machine learning due to recent theoretical and practical advances in these fields, and in particular Neural Networks (NN), which are known for their high performance in complex and nonlinear systems with large number of parameters and have been applied successfully in other areas of science and technology. In this paper we introduce NN to resonance control and compare initial performance results with traditional control techniques. An LCLS-II superconducting cavity type system is simulated in an FPGA, using the Cryomodule-on-Chip model developed by LBNL, and is used to evaluate machine learning algorithms. PB - JACoW Publishing CP - Geneva, Switzerland SP - 659 EP - 662 DA - 2019/10 PY - 2019 SN - 2673-7000 SN - 978-3-95450-223-3 DO - doi:10.18429/JACoW-NAPAC2019-WEPLM01 UR - http://jacow.org/napac2019/papers/weplm01.pdf ER -