NAPAC2019 - List of Authors (Diaz Cruz, J.A.)

Paper	Title	Page
WEPLM01	Studies in Applying Machine Learning to Resonance Control in Superconducting RF Cavities	659
SUPLO01	use link to see paper's listing under its alternate paper code
	J.A. Diaz Cruz, S. Biedron, M. Martinez-Ramon, R. Pirayesh, S.I. Sosa Guitron University of New Mexico, Albuquerque, USA J.A. Diaz Cruz SLAC, Menlo Park, California, USA
	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.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-WEPLM01
About •	paper received ※ 05 September 2019 paper accepted ※ 15 September 2019 issue date ※ 08 October 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

WEPLM01

Studies in Applying Machine Learning to Resonance Control in Superconducting RF Cavities

659

SUPLO01

use link to see paper's listing under its alternate paper code

J.A. Diaz Cruz, S. Biedron, M. Martinez-Ramon, R. Pirayesh, S.I. Sosa Guitron
University of New Mexico, Albuquerque, USA
J.A. Diaz Cruz
SLAC, Menlo Park, California, USA

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.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-WEPLM01

About •

paper received ※ 05 September 2019 paper accepted ※ 15 September 2019 issue date ※ 08 October 2019

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)