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| URL | https://doi.org/10.18429/JACoW-LINAC2024-TUPB007 |
|---|---|
| Title | Machine learning enabled model predictive control for the resonance frequency of the FRIB RFQ |
| Authors |
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| Abstract | Efficient control of frequency detuning for the radio-frequency quadrupole (RFQ) at the Facility for Rare Isotope Beams (FRIB) is still challenging. The transport delay and the complicated heat transfer process in the cooling water control system convolute the control problem. In this work, a long-short term memory (LSTM)-based Koopman model is proposed to deal with this time-delayed control problem. By learning the time-delayed correlations hidden in the historical data, this model can predict the behavior of RFQ frequency detuning with given control actions. With this model, a model predictive control (MPC) strategy is developed to pursue better control performance. |
| Paper | download: TUPB007.pdf |
| Cite | BibTeX, LaTeX, Text/Word, RIS, EndNote |
| Conference | 32nd Linear Accelerator Conference (LINAC2024) |
| Series | |
| Location | Chicago, IL, USA |
| Date | 25-30 Aug 2024 |
| Publisher | JACoW Publishing, Geneva, Switzerland |
| Editorial Board | Kelly Jaje (ANL), Kip Bishofberger (LANL), Ryan Brody (ANL), Gustavo Bruno (ANL), Jan Chrin (PSI), Cathy Eyberger (ANL), Maggie Montes (SLAC), Denise Skiadopoulos (ANL), Ling Wang (MSU/FRIB), Kent Wootton (ANL) |
| Online ISBN | 978-3-95450-219-6 |
| Online ISSN | 2226-0366 |
| Received | 19 August 2024 |
| Revised | 28 August 2024 |
| Accepted | 28 August 2024 |
| Issued | 23 October 2024 |
| DOI | 10.18429/JACoW-LINAC2024-TUPB007 |
| Pages | 338-340 |
| 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. |