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Real-time virtual circuits for plasma shape control via neural network emulators: integration and testing in the MAST-U PCS

arXiv · AI, language, vision and robotics · article · Aug 26, 2026 · UTC

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++ infer

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