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

@inproceedings{wang:icalepcs2023-tumbcmo14,
  author       = {F.Y. Wang and J. Cruz},
  title        = {{Initial Test of a Machine Learning Based SRF Cavity Active Resonance Control}},
% booktitle    = {Proc. ICALEPCS'23},
  booktitle    = {Proc. 19th Int. Conf. Accel. Large Exp. Phys. Control Syst. (ICALEPCS'23)},
  eventdate    = {2023-10-09/2023-10-13},
  pages        = {379--381},
  paper        = {TUMBCMO14},
  language     = {english},
  keywords     = {cavity, controls, SRF, resonance, simulation},
  venue        = {Cape Town, South Africa},
  series       = {International Conference on Accelerator and Large Experimental Physics Control Systems},
  number       = {19},
  publisher    = {JACoW Publishing, Geneva, Switzerland},
  month        = {02},
  year         = {2024},
  issn         = {2226-0358},
  isbn         = {978-3-95450-238-7},
  doi          = {10.18429/JACoW-ICALEPCS2023-TUMBCMO14},
  url          = {https://jacow.org/icalepcs2023/papers/tumbcmo14.pdf},
  abstract     = {{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. }},
}