用深度学习和强化学习实现粒子加速器真空压力自主调控。
Autonomous Pressure Control in MuVacAS via Deep Reinforcement Learning and Deep Learning Surrogate Models
- 构建真实数据训练的代理模型，模拟氩气注入动态。
- 强化学习智能体在扰动下仍能将压力控制在限值内。
- 适合下一代高要求粒子加速器的智能化控制系统研究者。
核聚变研发需要能承受极端条件的材料，IFMIF-DONES高功率粒子加速器正用于材料验证。其关键原型MuVacAS复现了加速器束流线末端段，需精确控制超真空腔内氩气压力。本文提出全数据驱动的自主压力调控方法：基于真实运行数据训练深度学习代理模型，模拟氩气注入系统动态；该高保真数字孪生作为快速仿真环境，用于训练深度强化学习智能体。结果表明，智能体成功学习到控制策略，在动态扰动下仍能将气体压力维持在严格限值内。该方法标志着向下一代粒子加速器所需的智能自主控制系统迈出重要一步。
原文摘要 · Abstract (English)
The development of nuclear fusion requires materials that can withstand extreme conditions. The IFMIF-DONES facility, a high-power particle accelerator, is being designed to qualify these materials. A critical testbed for its development is the MuVacAS prototype, which replicates the final segment of the accelerator beamline. Precise regulation of argon gas pressure within its ultra-high vacuum chamber is vital for this task. This work presents a fully data-driven approach for autonomous pressure control. A Deep Learning Surrogate Model, trained on real operational data, emulates the dynamics of the argon injection system. This high-fidelity digital twin then serves as a fast-simulation environment to train a Deep Reinforcement Learning agent. The results demonstrate that the agent successfully learns a control policy that maintains gas pressure within strict operational limits despite dynamic disturbances. This approach marks a significant step toward the intelligent, autonomous control systems required for the demanding next-generation particle accelerator facilities.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。