Initial experimental results of a machine learning-based temperature control system for an RF gun
physics.acc-ph
/ Authors
A. L. Edelen, S. G. Biedron, S. V. Milton, B. E. Chase, D. J. Crawford, N. Eddy, D. Edstrom, E. R. Harms, J. Ruan, J. K. Santucci
and 1 more author
/ Abstract
Colorado State University (CSU) and Fermi National Accelerator Laboratory (Fermilab) have been developing a control system to regulate the resonant frequency of an RF electron gun. As part of this effort, we present initial test results for a benchmark temperature controller that combines a machine learning-based model and a predictive control algorithm. This is part of an on-going effort to develop adaptive, machine learning-based tools specifically to address control challenges found in particle accelerator systems.