Initial experimental results of a machine learning-based temperature control system for an RF gun

A. L. Edelen; S. G. Biedron; S. V. Milton; B. E. Chase; D. J. Crawford; N. Eddy; D. Edstrom Jr.; E. R. Harms; J. Ruan; J. K. Santucci; P. Stabile

arXiv · 1511.01883

Initial experimental results of a machine learning-based temperature control system for an RF gun

A. L. Edelen·S. G. Biedron·S. V. Milton·B. E. Chase·D. J. Crawford·N. Eddy·D. Edstrom Jr.·E. R. Harms·J. Ruan·J. K. Santucci·P. Stabile

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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.

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A. L. Edelen, S. G. Biedron, S. V. Milton, B. E. Chase, D. J. Crawford, N. Eddy, D. Edstrom Jr., E. R. Harms, J. Ruan, J. K. Santucci, P. Stabile. 2015-11-05. Initial experimental results of a machine learning-based temperature control system for an RF gun. https://arxiv.org/abs/1511.01883

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