Problem
Research questions and friction points this paper is trying to address.
plasma shape control
virtual circuits
real-time
neural network emulators
tokamaks
Real-time virtual circuits for plasma shape control via neural network emulators: experimental demonstration on MAST Upgrade
🤖 AI Summary
研究通过使用神经网络模拟器实现实时虚拟电路，解决了托卡马克等离子体形状控制问题，实验在MAST Upgrade上验证了该方法的有效性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
real-time virtual circuits
neural network emulators
plasma shape control
MAST Upgrade
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