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.
@article{arxiv.1511.01883,
title = {Initial experimental results of a machine learning-based temperature control system for an RF gun},
author = {A. L. Edelen and S. G. Biedron and S. V. Milton and B. E. Chase and D. J. Crawford and N. Eddy and D. Edstrom and E. R. Harms and J. Ruan and J. K. Santucci and P. Stabile},
journal= {arXiv preprint arXiv:1511.01883},
year = {2016}
}