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@inproceedings{pirayesh:ipac2021-thpab268,
  author       = {R. Pirayesh and S. Biedron and J.A. Diaz Cruz and M. Martínez-Ramón},
  title        = {{Hierarchical Intelligent Real-Time Optimal Control for LLRF Using Time Series Machine Learning Methods and Transfer Learning}},
  booktitle    = {Proc. IPAC'21},
  pages        = {4329--4331},
  eid          = {THPAB268},
  language     = {english},
  keywords     = {controls, LLRF, cavity, network, simulation},
  venue        = {Campinas, SP, Brazil},
  series       = {International Particle Accelerator Conference},
  number       = {12},
  publisher    = {JACoW Publishing, Geneva, Switzerland},
  month        = {08},
  year         = {2021},
  issn         = {2673-5490},
  isbn         = {978-3-95450-214-1},
  doi          = {10.18429/JACoW-IPAC2021-THPAB268},
  url          = {https://jacow.org/ipac2021/papers/thpab268.pdf},
  note         = {https://doi.org/10.18429/JACoW-IPAC2021-THPAB268},
  abstract     = {{Machine learning (ML) has recently been applied to Low-level RF (LLRF) control systems to keep the voltage and phase of Superconducting Radiofrequency (SRF) cavities stable within 0.01 degree in phase and 0.01% amplitude as constraints. Model predictive control (MPC) uses an optimization algorithm offline to minimize a cost function with constraints on the states and control input. The surrogate model optimally controls the cavities online. Time series deep ML structures including recurrent neural network (RNN) and long short-term memory (LSTM) can model the control input of MPC and dynamics of LLRF as a surrogate model. When the predicted states diverge from the measured states more than a threshold at each time step, the states’ measurements from the cavity fine-tune the surrogate model with transfer learning. MPC does the optimization offline again with the updated surrogate model, and, next, transfer learning fine-tunes the surrogate model with the new data from the optimal control inputs. The surrogate model provides us with a computationally faster and accurate modeling of MPC and LLRF, which in turn results in a more stable control system.}},
}