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URLhttps://doi.org/10.18429/JACoW-IPAC2024-TUPS61
TitlePreliminary results on the reinforcement learning-based control of the microbunching instability
Authors
  • L. Scomparin, A. Santamaria Garcia, A. Kopmann, A. Mueller, C. Xu, E. Blomley, E. Bruendermann, J. Steinmann, J. Becker, M. Schuh, M. Caselle, T. Dritschler
    Karlsruhe Institute of Technology
  • A. Mochihashi, M. Weber
    Karlsruhe Instutute of Technology
AbstractReinforcement Learning (RL) has demonstrated its effectiveness in solving control problems in particle accelerators. A challenging application is the control of the microbunching instability (MBI) in synchrotron light sources. Here the interaction of an electron bunch with its emitted coherent synchrotron radiation leads to complex non-linear dynamics and pronounced fluctuations. Addressing the control of intricate dynamics necessitates meeting stringent microsecond-level real-time constraints. To achieve this, RL algorithms must be deployed on a high-performance electronics platform. The KINGFISHER system, utilizing the AMD-Xilinx Versal family of heterogeneous computing devices, has been specifically designed at KIT to tackle these demanding conditions. The system implements an experience accumulator architecture to perform online learning purely through interaction with the accelerator while still satisfying strong real-time constraints. The preliminary results of this innovative control paradigm at the Karlsruhe Research Accelerator (KARA) will be presented. Notably, this represents the first experimental attempt to control the MBI with RL using online training only.
Paperdownload: TUPS61.pdf
CiteBibTeX, LaTeX, Text/Word, RIS, EndNote
Conference15th International Particle Accelerator Conference
Series
LocationNashville, TN
Date19-24 May 2024
PublisherJACoW Publishing, Geneva, Switzerland
Editorial BoardFulvia Pilat - Oak Ridge National Laboratory Wolfram Fischer - Brookhaven National Laboratory Robert Saethre - Oak Ridge National Laboratory Petr Anisimov - Los Alamos National Laboratory Ivan Andrian - Elettra-Sincrotrone Trieste S.C.p.A.
Online ISBN978-3-95450-247-9
Online ISSN2673-5490
Received14 May 2024
Revised29 May 2024
Accepted29 May 2024
Issued01 July 2024
DOI10.18429/JACoW-IPAC2024-TUPS61
Pages1808-1811