AI 中文总结
本研究在MAST Upgrade装置上首次实验验证了基于神经网络模拟器的实时虚拟电路可实现等离子体形状控制，为简化托卡马克控制工作流程提供了可行方案。
AI 中文摘要
传统托卡马克装置中的等离子体形状控制依赖虚拟电路（VCs），这类虚拟电路通过对少量定制参考平衡态进行线性化后离线计算得到，并作为精心准备的调度方案在放电过程中部署。本文报告了实时虚拟电路的首次实验部署：我们用基于等离子体响应代理模型实时更新的虚拟电路取代预设查找表，同时保留了现有控制架构及基于虚拟电路控制的可解释性。此前研究已表明神经网络模拟器可生成高精度虚拟电路，并在闭环形状控制模拟中验证了其性能；本文则报告了其在MAST Upgrade（MAST-U）上的首次实验验证。涵盖预设形状扰动、反馈驱动偏滤器 leg 运动、强演化等离子体构型等不同场景的专项实验显示，实时虚拟电路可在MAST-U等离子体控制系统中完成等离子体形状控制任务。这些结果确立了实时线性化作为托卡马克传统等离子体形状控制实用扩展方案的实验可行性，本次实现也朝着更简化的控制工作流程迈出了关键一步：该流程将手动构建的分阶段虚拟电路调度方案替换为从训练好的代理模型自动在线生成的虚拟电路，无需针对特定场景重新训练。
英文摘要
Conventional plasma shape control in tokamaks relies on virtual circuits (VCs) that are computed offline from linearisations around a small, tailored number of reference equilibria, and deployed as expertly prepared schedules during the discharge. Here, we report on the first experimental deployment of real-time VCs. We replace pre-set look up tables with VCs updated in real time using surrogates of the plasma response. Both the existing control architecture and the interpretability of VC-based control are retained. Previous work showed that neural network emulators can produce accurate VCs, and validated their performance in closed-loop shape control simulations. Here, we report their first experimental validation on MAST Upgrade (MAST-U). Dedicated experiments spanning different scenarios, including prescribed shape perturbations, feedback-driven divertor-leg motion, and strongly evolving plasma configurations, show that real-time VCs can realise plasma shape control tasks within the MAST-U plasma control system. These results establish the experimental feasibility of real-time linearisations as a practical extension of conventional plasma shape control in tokamaks. The present implementation demonstrates a central step towards a simpler control workflow, in which manually constructed, phased VC schedules are replaced by VCs generated automatically online from a trained surrogate model, without scenario-specific retraining.
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