JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@unpublished{diazcruz:napac2022-thxd5,
author = {J.A. Diaz Cruz and S. Biedron},
title = {{Machine Learning-Based Tuning of Control Parameters for LLRF System of Superconducting Cavities}},
% booktitle = {Proc. NAPAC'22},
booktitle = {Proc. 5th Int. Particle Accel. Conf. (NAPAC'22)},
language = {english},
intype = {presented at the},
series = {International Particle Accelerator Conference},
number = {5},
venue = {Albuquerque, NM, USA},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {10},
year = {2022},
note = {presented at NAPAC'22 in Albuquerque, NM, USA, unpublished},
abstract = {{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. Low-Level RF (LLRF) systems can be implemented using proportional-integral (PI) 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).}},
}