  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.
