19–24 May 2024
Music City Center
US/Central timezone

Machine learning enabled model predictive control of the FRIB RFQ

TUPS57
21 May 2024, 16:00
2h
Blues (MCC Exhibit Hall A)

Blues

MCC Exhibit Hall A

Poster Presentation MC6.D13 Machine Learning Tuesday Poster Session

Speaker

Jinyu Wan (Facility for Rare Isotope Beams)

Description

Efficient control of frequency detuning for the radio-frequency quadrupole (RFQ) at the Facility for Rare Isotope Beams (FRIB) is still challenging. The transport delay and the complicated heat transfer process in the cooling water control system convolute the control problem. In this work, a long-short term memory (LSTM)-based Koopman model is proposed to deal with this time-delayed control problem. By learning the time-delayed correlations hidden in the historical data, this model can predict the behavior of RFQ frequency detuning with given control actions. With this model, a model predictive control (MPC) strategy is developed to pursue better control performance.

Funding Agency

Work supported by the U.S. Department of Energy Office of Science under Cooperative Agreement DE-SC0023633, the State of Michigan, and Michigan State University.

Region represented North America

Primary author

Jinyu Wan (Facility for Rare Isotope Beams)

Co-authors

Shen Zhao (Facility for Rare Isotope Beams) Yue Hao (Facility for Rare Isotope Beams) Wei Chang (Facility for Rare Isotope Beams) Hiroyuki Ao (Facility for Rare Isotope Beams, Michigan State University)

Presentation materials

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