Author: Pirayesh, R.
Paper Title Page
TUPOST027 Machine Learning-Based Tuning of Control Parameters for LLRF System of Superconducting Cavities 915
 
  • J.A. Diaz Cruz, S. Biedron
    UNM-ECE, Albuquerque, USA
  • J.A. Diaz Cruz
    SLAC, Menlo Park, California, USA
  • R. Pirayesh
    UNM-ME, Albuquerque, New Mexico, USA
  • S. Sosa
    ODU, Norfolk, Virginia, USA
 
  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.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST027  
About • Received ※ 14 June 2022 — Revised ※ 15 June 2022 — Accepted ※ 16 June 2022 — Issue date ※ 20 June 2022
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