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

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Physics > Plasma Physics

arXiv:2608.26216 (physics)
[Submitted on 26 Aug 2026]

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

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Abstract: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.
Comments: 8 pages, 3 figures, presented at 15th Technical Meeting on Control Systems, Data Acquisition, Data Management and Remote Participation in Fusion Research and submitted for review at Fusion Engineering and Design on July 31 2026
Subjects: Plasma Physics (physics.plasm-ph); Machine Learning (cs.LG)
Cite as: arXiv:2608.26216 [physics.plasm-ph]
  (or arXiv:2608.26216v1 [physics.plasm-ph] for this version)
  https://doi.org/10.48550/arXiv.2608.26216
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Matthew Marshall [view email]
[v1] Wed, 26 Aug 2026 09:59:17 UTC (1,491 KB)
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