Adaptive optimization and control using neural networks

Mead, W. C.; Brown, Stanley K.; Jones, R.D.; Bowling, P.S.; Barnes, Cris W.

doi:10.1016/0168-9002(94)91530-x

Cited by 5 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Much early discussion during the late 1980s and early 1990s focused on applying rule-based systems to accelerator control and tuning [29][30][31][32][33]. In the early 1990s, scientists at Los Alamos National Lab had some experimental success with neural-network-based ion source control [34][35][36]. Other early studies at the University of New Mexico focused on orbit/trajectory control [37][38][39][40][41], fault detection and management [42,43], and root-cause analysis of errors (e.g.…”

Section: Early History Of Usage For Particle Acceleratorsmentioning

confidence: 99%

Opportunities in Machine Learning for Particle Accelerators

Edelen,

Mayes,

Bowring

et al. 2018

Preprint

View full text Add to dashboard Cite

Machine learning" (ML) is a subfield of artificial intelligence. The term applies broadly to a collection of computational algorithms and techniques that train systems from raw data rather than a priori models. ML techniques are now technologically mature enough to be applied to particle accelerators, and we expect that ML will become an increasingly valuable tool to meet new demands for beam energy, brightness, and stability. The intent of this white paper is to provide a high-level introduction to problems in accelerator science and operation where incorporating ML-based approaches may provide significant benefit. We review ML techniques currently being investigated at particle accelerator facilities, and we place specific emphasis on active research efforts and promising exploratory results. We also identify new applications and discuss their feasibility, along with the required data and infrastructure strategies. We conclude with a set of guidelines and recommendations for laboratory managers and administrators, emphasizing the logistical and technological requirements for successfully adopting this technology. This white paper also serves as a summary of the discussion from a recent workshop held at SLAC on ML for particle accelerators [1].

show abstract

Section: Early History Of Usage For Particle Acceleratorsmentioning

confidence: 99%

Opportunities in Machine Learning for Particle Accelerators

Edelen,

Mayes,

Bowring

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…In the early 1990s at Los Alamos, a NN-based PID tuner for a low level RF system was implemented [74]. Also at Los Alamos, several neural network schemes were used to control a negative ion source [75,76,77].…”

Section: ) Previous Efforts To Apply Neural Network To Particle Accmentioning

confidence: 99%

Neural Networks for Modeling and Control of Particle Accelerators

Edelen

Biedron

Chase

et al. 2016

IEEE Trans. Nucl. Sci.

View full text Add to dashboard Cite

Abstract-Particle accelerators are host to myriad nonlinear and complex physical phenomena. 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. Many early attempts to apply neural networks to particle accelerators yielded mixed results, due to the relative immaturity of the technology for such tasks. 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 between Fermilab and Colorado State University (CSU). We 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.

show abstract

“…Training the neural net can be tricky and slow, but amazing things can be done if one perseveres. The control of a negative ion source [22] at Los Alamos National Laboratory provides an excellent example of the use of a neural net. Another example uses a neural net (in simulation only) to correct the orbit in storage rings at Brookhaven National Laboratory [23].…”

Section: Adaptive Filters and Controlmentioning

confidence: 99%

FEEDBACK: Theory and Accelerator Applications

Himel

1997

Annu. Rev. Nucl. Part. Sci.

View full text Add to dashboard Cite

The use of feedback to stabilize the beam and improve the performance of accelerators is becoming more common. The methods used to design the feedback algorithms are introduced and some practical implementation details are described. The design of a PID loop using classical control techniques is covered as is the design of an optimal controller using modern control theory. Some adaptive control techniques are also briefly described. Examples are given of multiple-input-multiple-output loops and of how to handle systems of many interacting feedback loops.

show abstract

Adaptive optimization and control using neural networks

Cited by 5 publications

References 4 publications

Opportunities in Machine Learning for Particle Accelerators

Opportunities in Machine Learning for Particle Accelerators

Neural Networks for Modeling and Control of Particle Accelerators

FEEDBACK: Theory and Accelerator Applications

Contact Info

Product

Resources

About