| Title |
Achieving Optimal Control of LLRF Control System with Artificial Intelligence |
| Authors |
- R. Pirayesh, S. Biedron, J.A. Diaz Cruz, M. Martinez-Ramon, S.I. Sosa Guitron
University of New Mexico, Albuquerque, New Mexico, USA
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| Abstract |
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.
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| Paper |
download MOPHA114.PDF [0.831 MB / 5 pages] |
| Poster |
download MOPHA114_POSTER.PDF [0.847 MB] |
| Export |
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| Conference |
ICALEPCS2019 |
| Series |
International Conference on Accelerator and Large Experimental Physics Control Systems (17th) |
| Location |
New York, NY, USA |
| Date |
05-11 October 2019 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Karen S. White (ORNL, Oak Ridge, TN, USA); Kevin A. Brown (BNL, Upton, NY, USA); Philip S. Dyer (BNL, Upton, NY, USA); Volker RW Schaa (GSI, Darmstadt, Germany) |
| Online ISBN |
978-3-95450-209-7 |
| Online ISSN |
2226-0358 |
| Received |
09 October 2019 |
| Accepted |
10 October 2019 |
| Issue Date |
30 August 2020 |
| DOI |
doi:10.18429/JACoW-ICALEPCS2019-MOPHA114 |
| Pages |
488-492 |
| Copyright |
 Published by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |
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