# Enabling Integrated AI Control on DIII-D: A Control System Design with State-of-the-art Experiments

arXiv: https://arxiv.org/abs/2511.08818 · HTML: https://mlsanity.com/paper/2511.08818/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2025.

## Built on by (most cited first, 1 of 1)

- [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)

We present the design and application of a general algorithm for Prediction And Control using MAchiNe learning (PACMAN) in DIII-D. Machine learing (ML)-based predictors and controllers have shown great promise in achieving regimes in which traditional controllers fail, such as tearing mode free scenarios, ELM-free scenarios and stable advanced tokamak conditions. The architecture presented here was deployed on DIII-D to facilitate the end-to-end implementation of advanced control experiments, from diagnostic processing to final actuation commands. This paper describes the detailed design of the algorithm and explains the motivation behind each design point. We also describe several successful ML control experiments in DIII-D using this algorithm, including a reinforcement learning controller targeting advanced non-inductive plasmas, a wide-pedestal quiescent H-mode ELM predictor, an Alfv'en Eigenmode controller, a Model Predictive Control plasma profile controller and a state-machine Tearing Mode predictor-controller. There is also discussion on guiding principles for real-time machine learning controller design and implementation.