Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e
CoRR(2023)
摘要
We introduce a novel Proximal Policy Optimization (PPO) algorithm aimed at
addressing the challenge of maintaining a uniform proton beam intensity
delivery in the Muon to Electron Conversion Experiment (Mu2e) at Fermi National
Accelerator Laboratory (Fermilab). Our primary objective is to regulate the
spill process to ensure a consistent intensity profile, with the ultimate goal
of creating an automated controller capable of providing real-time feedback and
calibration of the Spill Regulation System (SRS) parameters on a millisecond
timescale. We treat the Mu2e accelerator system as a Markov Decision Process
suitable for Reinforcement Learning (RL), utilizing PPO to reduce bias and
enhance training stability. A key innovation in our approach is the integration
of a neuralized Proportional-Integral-Derivative (PID) controller into the
policy function, resulting in a significant improvement in the Spill Duty
Factor (SDF) by 13.6
baseline by an additional 1.6
results based on a differentiable simulator of the Mu2e accelerator. It paves
the groundwork for real-time implementations and applications, representing a
crucial step towards automated proton beam intensity control for the Mu2e
experiment.
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