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| URL | https://doi.org/10.18429/JACoW-IPAC2024-MOPS66 |
|---|---|
| Title | First operational experience with data-driven hysteresis compensation for the main dipole magnets of the CERN SPS |
| Authors |
|
| Abstract | Magnetic hysteresis, eddy currents, and manufacturing imperfections pose significant challenges for beam operation in multi-cycling synchrotrons. Addressing the dynamic dependency of magnetic fields on cycling history is a current limitation for control room tools using existing models. This paper outlines recent advancements to solve this, presenting the outcome of operational tests utilizing data-driven approaches and an overview of the next steps. Notably, artificial neural networks, including long short-term memory networks, transformers and other time series analysis architectures, are employed to model static and dynamic effects in the main dipole magnets of the CERN SPS. These networks capture hysteresis and eddy current decays based on measured magnetic field and data from the real-time magnetic measurement system of the SPS main dipoles. Cycle-by-cycle feed-forward corrections are implemented through the CERN accelerator controls infrastructure, which propagate corrections of magnetic fields to corresponding adjustments in the current of the power converters feeding the magnets. |
| Paper | download: MOPS66.pdf |
| Cite | BibTeX, LaTeX, Text/Word, RIS, EndNote |
| Conference | 15th International Particle Accelerator Conference |
| Series | |
| Location | Nashville, TN |
| Date | 19-24 May 2024 |
| Publisher | JACoW Publishing, Geneva, Switzerland |
| Editorial Board | Fulvia 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 ISBN | 978-3-95450-247-9 |
| Online ISSN | 2673-5490 |
| Received | 14 May 2024 |
| Revised | 18 May 2024 |
| Accepted | 21 May 2024 |
| Issued | 01 July 2024 |
| DOI | 10.18429/JACoW-IPAC2024-MOPS66 |
| Pages | 874-877 |
| Copyright | Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |