skip to main content

DOE PAGESDOE PAGES

This content will become publicly available on April 1, 2017

Title: Neural Networks for Modeling and Control of Particle Accelerators

Myriad nonlinear and complex physical phenomena are host to particle accelerators. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Moreover, many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. For the purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration betweenmore » Fermilab and Colorado State University (CSU). We also describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.« less
Authors:
 [1] ;  [1] ;  [2] ;  [2] ;  [1] ;  [2]
  1. Colorado State Univ., Fort Collins, CO (United States)
  2. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Publication Date:
OSTI Identifier:
1260272
Report Number(s):
FERMILAB-PUB--16-121-AD
Journal ID: ISSN 0018-9499; 1466580
Grant/Contract Number:
AC02-07CH11359
Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Nuclear Science
Additional Journal Information:
Journal Volume: 63; Journal Issue: 2; Journal ID: ISSN 0018-9499
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Research Org:
Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
Country of Publication:
United States
Language:
English
Subject:
43 PARTICLE ACCELERATORS article intelligence; machine learning; particle accelerators; control systems; predictive control; adaptive control