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RIS citation export for TUMBCMO14: Initial Test of a Machine Learning Based SRF Cavity Active Resonance Control

TY  - CONF
AU  - Wang, F.Y.
AU  - Cruz, J.
ED  - Schaa, Volker RW
ED  - Götz, Andy
ED  - Venter, Johan
ED  - White, Karen
ED  - Robichon, Marie
ED  - Rowland, Vivienne
TI  - Initial Test of a Machine Learning Based SRF Cavity Active Resonance Control
J2  - Proc. of ICALEPCS2023, Cape Town, South Africa, 09-13 October 2023
CY  - Cape Town, South Africa
T2  - International Conference on Accelerator and Large Experimental Physics Control Systems
T3  - 19
LA  - english
AB  - 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. 
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 379
EP  - 381
KW  - cavity
KW  - controls
KW  - SRF
KW  - resonance
KW  - simulation
DA  - 2024/02
PY  - 2024
SN  - 2226-0358
SN  - 978-3-95450-238-7
DO  - doi:10.18429/JACoW-ICALEPCS2023-TUMBCMO14
UR  - https://jacow.org/icalepcs2023/papers/tumbcmo14.pdf
ER  -