基于神经网络模拟器的等离子体形状控制实时虚拟电路：在MAST-U PCS中的集成与测试
Real-time virtual circuits for plasma shape control via neural network emulators: integration and testing in the MAST-U PCS
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中文总结 AI 辅助
该研究将神经网络模拟器集成到MAST-U的PCS中，开发了用于等离子体形状控制的实时虚拟电路，经验证可应用于即将开展的MAST-U实验及未来聚变装置。
中文摘要 AI 辅助
在托卡马克实验中部署先进的、基于人工智能的控制算法，需要与现有的等离子体控制系统（PCS）架构实现可靠集成，并进行广泛的实验前验证。本文介绍了在MAST升级装置（MAST-U）的PCS环境中，用于等离子体形状控制的神经网络模拟虚拟电路的集成与测试工作。该神经网络模型利用等离子体电流、极向场线圈电流以及等离子体轮廓参数来预测等离子体形状。本文阐述了这些模型如何通过与PCS对接的实时C++推理服务器进行部署，返回形状预测结果及其雅可比矩阵，以及如何从雅可比矩阵计算虚拟电路矩阵和更新后的线圈电流请求，以实现实时驱动。研究重点在于验证工作流程和所采用的最佳实践，以确保在实验部署前对所提出的控制框架建立信心。本工作展示了用于聚变控制系统的实用型基于人工智能的形状控制组件，与即将开展的MAST-U实验及未来装置直接相关。
英文摘要
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.
发表机构
- Hartree Centre, STFC（哈特里中心（科学与技术设施委员会））
机构由 AI 辅助整理，请以论文原文为准。
