Abstract

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.

Highlights

  • Particle accelerators are host to myriad complex and nonlinear physical phenomena

  • We present an overview of some challenges in particle accelerator control, provide an overview of relevant artificial intelligence (AI) concepts, describe some ways in which we are applying these to accelerators, and present an example of our work at Fermilab Accelerator Science and Technology (FAST)

  • There is a clear need for the development and validation of reliable, adaptive control techniques for complex problems in particle accelerators

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Summary

Introduction

Particle accelerators are host to myriad complex and nonlinear physical phenomena Adding to this inherent complexity, they often involve a multitude of interacting systems, exhibit long-term process cycles, and endure changes in individual machine components over time. As increasingly high-intensity, high-energy, and high-gradient accelerators are built that fundamentally rely on increasingly complex/nonlinear phenomena, traditional control techniques become inadequate in some domains. Taken together, this leaves us with many challenges for designing control systems that will reliably meet performance demands for both present and future accelerators

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