Real-time virtual circuits for plasma shape control via neural network emulators

2026-05-14

AI Summary

This work addresses the limitations of conventional virtual circuits in tokamak plasma shape control, which rely on offline precomputation and struggle to adapt to real-time dynamic deviations, leading to degraded performance. The authors propose a deep neural network–based plasma shape parameter simulator trained on a Grad–Shafranov equilibrium database comprising over one million samples. This approach enables, for the first time, differentiable and highly accurate real-time generation of virtual circuits. By dynamically producing orthogonal virtual circuits conditioned on the current plasma state, the method overcomes the constraints of traditional offline scheduling and effectively decouples multivariable control. Experimental validation demonstrates that the generated circuits maintain high fidelity and strong decoupling across diverse equilibrium configurations, significantly enhancing the robustness and generalizability of real-time plasma shape control.

Abstract

Reliable position and shape control in tokamak plasmas requires accurate real-time regulation of several strongly coupled shape parameters. The control vectors that disentangle these couplings, referred to as \textit{virtual circuits} (VCs), enable independent shape parameter control for a specific Grad--Shafranov (GS) equilibrium. Numerical calculation of VCs is not currently feasible in real time, therefore VCs are usually computed prior to each experiment, using a small number of reference GS equilibria sampled along the desired scenario trajectory, with each VC used to control the plasma within a preset time interval. While effective near the reference equilibrium, this approach can lead to degraded performance as the plasma departs from the reference equilibrium and/or from the desired trajectory, and it complicates the design of robust control strategies for rapidly evolving plasma configurations. In this paper, we construct neural-network-based emulators of plasma shape parameters from which VCs can be derived, to provide the MAST Upgrade (MAST-U) plasma control system with state-aware VCs in real-time. To do this, we develop an extensive library of over a million simulated GS equilibria, covering a substantial portion of the MAST-U operational space. These emulators provide differentiable functions whose gradients can be rapidly computed, enabling the derivation of accurate VCs for real-time shape control. We perform extensive verification of the emulated VCs by testing whether they disentangle the control problem. The neural-network-based approach delivers high accuracy and orthogonality across a diverse range of equilibria. This work establishes the physical validity of emulated VCs as a scalable and general alternative to schedules of precomputed VCs.

Problem

• virtual circuits
• plasma shape control
• real-time control
• tokamak
• Grad-Shafranov equilibrium

Innovation

• neural network emulators
• virtual circuits
• plasma shape control
• real-time control
• Grad–Shafranov equilibrium