Extremum Seeking-Based Control System for Particle Accelerator Beam Loss Minimization

被引:14
作者
Scheinker, Alexander [1 ]
Huang, En-Chuan [1 ]
Taylor, Charles [1 ]
机构
[1] Los Alamos Natl Lab, Appl Electrodynam Grp, Los Alamos, NM 87545 USA
关键词
Particle beams; Loss measurement; Tuning; Radio frequency; Particle beam measurements; Magnetic hysteresis; Neutrons; Adaptive control; extremum seeking (ES); model-independent control; optimization; particle accelerator control; time-varying systems; ITERATIVE LEARNING CONTROL; STABILITY; FEEDBACK;
D O I
10.1109/TCST.2021.3136133
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Particle accelerators throughout the world vary widely in terms of age and availability of advanced noninvasive diagnostics that provide varying levels of detail about the accelerated beams. Beam loss measurements and current monitors are ubiquitous in the accelerator community, they are noninvasive, and they are some of the most important metrics in terms of preventing damage or irradiation of beam pipes and equipment for high-energy machines. However, beam loss and current measurements are difficult to use for feedback tuning because of a lack of a known analytic relationship between scalar loss measurements throughout an accelerator and the hundreds of thousands of parameters that influence the beam and time variation of the beam source itself. In this work, we present a model-independent extremum seeking (ES) controller, which has been implemented for automated tuning and optimization of the Los Alamos Neutron Science Center (LANSCE) linear ion accelerator based only on scalar quantities, such as beam loss or flux measurements. We demonstrate the approach on various accelerator subsystems, including groups of radio frequency (RF) accelerating cavities, quadrupole magnets, bending magnets, and steering magnets. This tool is now available as a use graphical user interface (GUI) in the LANSCE control room, allowing beam physicists to choose arbitrary groups of parameters and beam loss or current monitors for adaptive tuning.
引用
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页码:2261 / 2268
页数:8
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