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The radio-frequency quadrupole (RFQ) at the Facility for Rare Isotope Beams(FRIB) is a critical device to accelerate heavy ion beams from 12 keV/u to 0.5MeV/u for state-of-the-art nuclear physics experiments. Efficient control ofthe RFQ resonance frequency detuning still remains a challenge because thetemperature-sensitive frequency is solely control by a cooling water system,exhibiting complicated transport delay and nonlinearity in the heat transferprocesses. In this work, we propose a long-short term memory (LSTM)-basedKoopman network model that can simultaneously learn the time-delayed andnon-delayed correlations hidden in the historical operating data. It is proventhat the model can effectively predict the behavior of the RFQ resonancefrequency using historical data as inputs. With this model, a model predictivecontrol (MPC) framework based on the Newton-Raphson method is proposed andtested. We demonstrate that the MPC framework utilizing deep learning model isable to provide precise and rapid control for the RFQ frequency detuning,reducing the control time by half compared to theproportional-integral-derivative (PID) controller.

Time-Delayed Koopman Network-Based Model Predictive Control for the FRIB RFQ