# Real-time virtual circuits for plasma shape control via neural network emulators: experimental demonstration on MAST Upgrade

arXiv: https://arxiv.org/abs/2608.28468 · HTML: https://mlsanity.com/paper/2608.28468/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 28 Aug 2026.

## Builds on (most cited first, 3 of 3)

- [Towards practical reinforcement learning for tokamak magnetic control](https://mlsanity.com/paper/2307.11546/) (2023)
- [Real-time virtual circuits for plasma shape control via neural network emulators](https://mlsanity.com/paper/2605.14939/) (2026)
- [Real-time virtual circuits for plasma shape control via neural network emulators: integration and testing in the MAST-U PCS](https://mlsanity.com/paper/2608.26216/) (2026)

## Abstract (arXiv)

Conventional plasma shape control in tokamaks relies on virtual circuits (VCs) that are computed offline from linearisations around a small, tailored number of reference equilibria, and deployed as expertly prepared schedules during the discharge. Here, we report on the first experimental deployment of real-time VCs. We replace pre-set look up tables with VCs updated in real time using surrogates of the plasma response. Both the existing control architecture and the interpretability of VC-based control are retained. Previous work showed that neural network emulators can produce accurate VCs, and validated their performance in closed-loop shape control simulations. Here, we report their first experimental validation on MAST Upgrade (MAST-U). Dedicated experiments spanning different scenarios, including prescribed shape perturbations, feedback-driven divertor-leg motion, and strongly evolving plasma configurations, show that real-time VCs can realise plasma shape control tasks within the MAST-U plasma control system. These results establish the experimental feasibility of real-time linearisations as a practical extension of conventional plasma shape control in tokamaks. The present implementation demonstrates a central step towards a simpler control workflow, in which manually constructed, phased VC schedules are replaced by VCs generated automatically online from a trained surrogate model, without scenario-specific retraining.