Fuelling fusion plasmas with pellets: Can neuromorphic control outperform Sigma-Delta modulation?

TL;DR

This paper introduces a neuromorphic control approach for pellet injection in nuclear fusion, modeling the process as a hybrid system and comparing it to sigma-delta modulation, with formal analysis and simulations.

Contribution

It presents a novel neuron-inspired control method for pellet fuelling in fusion plasmas, addressing hybrid dynamics and providing stability analysis.

Findings

Neuromorphic controller effectively manages pellet injection dynamics.

Formal stability guarantees derived for both control methods.

Simulations validate the advantages of the neuromorphic approach.

Abstract

Nuclear fusion is a promising clean energy source in which deuterium and tritium fuse inside a magnetically confined plasma in a tokamak, releasing energy. A key challenge on the route to practical nuclear fusion is the control of the plasma density which has to be done through adding fuel in the form of deuterium and tritium to the plasma. Pellet injection, firing frozen fuel into the plasma, is used to accomplish this. Since the injection of a pellet causes an almost instantaneous increase in particle density compared to the time scales of the plasma dynamics, the problem is of a hybrid nature in which continuous plasma dynamics are interrupted by discrete bursts of particles. In this paper, we propose a formal hybrid model for this fuelling process and we propose a new, neuron-inspired control method that treats pellets much like spikes as in a brain-like system. The neuromorphic controller offers a lightweight solution that naturally fits the hybrid character of pellet fuelling. For comparison, we also develop a hybrid model of sigma-delta modulation, which is used in current tokamaks. For both the neuromorphic controller and the sigma-delta modulation we present formal analysis results for this control problem in nuclear fusion. We derive explicit actuator and controller parameter constraints, key for controller tuning, that lead to practical stability guarantees. Numerical simulations compare the different controller variants and validate the theoretical results.