Control of pedestal-top electron density using RMP and gas puff at KSTAR

TL;DR

This paper demonstrates a real-time control system for pedestal-top electron density in KSTAR using RMP and gas puff, achieving high accuracy and dynamic target tracking through machine learning and system identification.

Contribution

It introduces a fast, real-time control algorithm combining RMP and gas puff actuators with machine learning-based density reconstruction for plasma density regulation.

Findings

Achieved ~1.5% median error in density control

Controller can follow dynamic density targets effectively

System enables real-time density adjustments during plasma discharges

Abstract

We report the experimental results of controlling the pedestal-top electron density by applying resonant magnetic perturbation with the in-vessel control coils and the main gas puff in the 2024-2025 KSTAR experimental campaign. The density is reconstructed using a parametrized psi_N grid and the five channels of the line-averaged density measured by a two-colored interferometer. The reconstruction procedure is accelerated by deploying a multi-layer perceptron to run in about 120 microseconds and is fast enough for real-time control. A proportional-integration controller is adopted, with the controller gains being estimated from the system identification processes. The experimental results show that the developed controller can follow a dynamic target while exclusively using both actuators. The absolute percentage errors between the electron density at psi_N=0.89 and the target are approximately 1.5% median and a 2.5% average value. The developed controller can even lower the density by using the pump-out mechanism under RMP, and it can follow a more dynamic target than a single actuator controller. The developed controller will enable experimental scenario exploration within a shot by dynamically setting the density target or maintaining a constant electron density within a discharge.