IPAC'25 - the 16th International Particle Accelerator Conferece

Machine learning-based model predictive control of the FRIB SRF

THPM011

5 Jun 2025, 15:30

2h

Exhibiton Hall A _Magpie (TWTC)

Poster Presentation MC6.D13 Machine Learning Thursday Poster Session

Speaker

Jinyu Wan (Facility for Rare Isotope Beams)

Description

A machine learning-based model predictive control (MPC) application has been developed for the RFQ control at Facility for Rare Isotope Beams (FRIB). In this work, we extend this approach to broader applications at FRIB, the superconducting radio frequency (SRF) control. A machine learning model is trained to learn the correlations between the beam loss and the SRF signals. With the model, a MPC contoller is implemented to minimize the beam loss with high efficiency.

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.