JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{diazcruz:ipac2022-tupost027,
author = {J.A. Diaz Cruz and S. Biedron and R. Pirayesh and S. Sosa},
title = {{Machine Learning-Based Tuning of Control Parameters for LLRF System of Superconducting Cavities}},
booktitle = {Proc. IPAC'22},
% booktitle = {Proc. 13th International Particle Accelerator Conference (IPAC'22)},
pages = {915--917},
eid = {TUPOST027},
language = {english},
keywords = {cavity, controls, LLRF, simulation, SRF},
venue = {Bangkok, Thailand},
series = {International Particle Accelerator Conference},
number = {13},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {07},
year = {2022},
issn = {2673-5490},
isbn = {978-3-95450-227-1},
doi = {10.18429/JACoW-IPAC2022-TUPOST027},
url = {https://jacow.org/ipac2022/papers/tupost027.pdf},
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. 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.}},
}