用神经网络模拟等离子体形状控制电路，实现实时反馈。
Real-time virtual circuits for plasma shape control via neural network emulators: integration and testing in the MAST-U PCS
- 用神经网络预测等离子体形状及梯度，驱动实时控制。
- 在MAST-U装置上部署并验证了整套虚拟电路系统。
- 适合核聚变控制研究者和工程团队参考应用。
在托卡马克实验中部署先进的人工智能控制算法，需与现有等离子体控制系统（PCS）架构稳健集成，并进行充分的实验前验证。本文介绍了将神经网络模拟的虚拟控制电路集成到MAST升级装置（MAST-U）PCS环境中的工作。神经网络模型基于等离子体电流、极向场线圈电流及等离子体剖面参数，预测等离子体形状。论文说明了如何通过实时C++推理服务将模型接入PCS，返回形状预测及其雅可比矩阵；并利用该雅可比矩阵计算虚拟电路矩阵和更新后的线圈电流指令，用于实时执行。重点强调了验证流程与最佳实践，以确保实验部署前对控制框架的可靠性。本工作展示了可用于聚变控制系统的实用人工智能组件，对即将开展的MAST-U实验及未来装置具有直接意义。
原文摘要 · Abstract (English)
The deployment of advanced, AI-enabled control algorithms in tokamak experiments requires robust integration with existing plasma control system (PCS) architectures and extensive pre-experimental validation. In this contribution, we describe the integration and testing of neural-network-emulated virtual circuits for plasma shape control within the MAST Upgrade (MAST-U) PCS environment. The neural network models predict the plasma shape using the plasma current, poloidal field coil currents, and plasma profile parameters. In this paper, we explain how they are deployed via a real-time C++ inference server that interfaces with the PCS, returning the shape prediction and its Jacobian, and how, from the latter, virtual circuit matrices and updated coil current requests are computed for real-time actuation. Emphasis is placed on the validation workflow and best practices adopted to ensure confidence in the proposed control framework prior to experimental deployment. This work demonstrates practical AI-based shape control components for fusion control systems, with direct relevance for upcoming MAST-U experiments and future devices.
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