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@InProceedings{pirayesh:icalepcs2019-mopha114,
author = {R. Pirayesh and S. Biedron and J.A. Diaz Cruz and M. Martinez-Ramon and S.I. Sosa Guitron},
title = {{Achieving Optimal Control of LLRF Control System with Artificial Intelligence}},
booktitle = {Proc. ICALEPCS'19},
pages = {488--492},
paper = {MOPHA114},
language = {english},
keywords = {controls, cavity, LLRF, SRF, framework},
venue = {New York, NY, USA},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {17},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2020},
issn = {2226-0358},
isbn = {978-3-95450-209-7},
doi = {10.18429/JACoW-ICALEPCS2019-MOPHA114},
url = {https://jacow.org/icalepcs2019/papers/mopha114.pdf},
note = {https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA114},
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.},
}