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@inproceedings{diazcruz:ipac2021-mopab290,
author = {J.A. Diaz Cruz and S. Biedron and M. Martínez-Ramón and R. Pirayesh and S. Sosa},
title = {{Machine Learning-Based LLRF and Resonance Control of Superconducting Cavities}},
booktitle = {Proc. IPAC'21},
pages = {920--923},
eid = {MOPAB290},
language = {english},
keywords = {cavity, controls, LLRF, simulation, SRF},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-MOPAB290},
url = {https://jacow.org/ipac2021/papers/mopab290.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-MOPAB290},
abstract = {{Superconducting radio frequency (SRF) cavities with high loaded quality factors that operate in continuous wave (CW) and low beam loading are sensitive to microphonics-induced detuning. Cavity detuning can result in an increase of operational power and/or in a cavity quench. Such SRF cavities have bandwidths on the order of 10 Hz and detuning requirements can be as tight as 10 Hz. Passive methods to mitigate vibration sources and their impact in the cryomodule/cavity environment are widely used. Active resonance control techniques that use stepper motors and piezoelectric actuators to tune the cavity resonance frequency by compensating for microphonics detuning have been investigated. These control techniques could be further improved by applying Machine Learning (ML), which has shown promising results in other particle accelerator control systems. In this paper, we describe a Low-level RF (LLRF) and resonance control system based on ML methods that optimally and adaptively tunes the control parameters. We present simulations and test results obtained using a low power test bench with a cavity emulator.}},
}