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| TUMBCMO14 |
Initial Test of a Machine Learning Based SRF Cavity Active Resonance Control |
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- F.Y. Wang, J. Cruz
SLAC, Menlo Park, California, USA
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We’ll introduce a high precision active motion controller based on machine learning (ML) technology and electric piezo actuator. The controller will be used for SRF cavity active resonance control, where a data-driven model for system motion dynamics will be developed first, and a model predictive controller (MPC) will be built accordingly. Simulation results as well as initial test results with real SRF cavities will be presented in the paper.
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| DOI • |
reference for this paper
※ doi:10.18429/JACoW-ICALEPCS2023-TUMBCMO14
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| About • |
Received ※ 03 October 2023 — Revised ※ 14 November 2023 — Accepted ※ 27 November 2023 — Issued ※ 09 December 2023 |
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