| Title |
Initial Experimental Results of a Machine Learning-Based Temperature Control System for an RF Gun |
| Authors |
- A.L. Edelen, S. Biedron, S.V. Milton
CSU, Fort Collins, Colorado, USA
- B.E. Chase, D.J. Crawford, N. Eddy, D.R. Edstrom, E.R. Harms, J. Ruan, J.K. Santucci, P. Stabile
Fermilab, Batavia, Illinois, USA
|
| 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 experimental results for a benchmark temperature controller that combines a machine learning-based model and a predictive control algorithm for improved settling time, overshoot, and disturbance rejection relative to conventional techniques. Such improvements have implications for machine up-time and management of reflected power. This work is part of an on-going effort to develop adaptive, machine learning-based tools specifically to address control challenges found in particle accelerator systems.
|
| Paper |
download MOPWI028.PDF [0.897 MB / 3 pages] |
| Export |
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| Conference |
IPAC2015, Richmond, VA, USA |
| Series |
International Particle Accelerator Conference (6th) |
| Proceedings |
Link to full IPAC2015 Proccedings |
| Session |
Monday Posters (Wilson) |
| Date |
04-May-15 16:00–18:00 |
| Main Classification |
6: Beam Instrumentation, Controls, Feedback, and Operational Aspects |
| Sub Classification |
T27 - Low Level RF |
| Keywords |
controls, gun, cavity, monitoring, network |
| Publisher |
JACoW, Geneva, Switzerland |
| Editors |
Stuart Henderson (ANL, Argonne, IL, USA); Evelyn Akers (Jlab, Newport News, VA, USA); Todd Satogata (JLab, Newport News, VA, USA); Volker R.W. Schaa (GSI, Darmstadt, Germany) |
| ISBN |
978-3-95450-168-7 |
| Published |
June 2015 |
| Copyright |
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