Density regulation with disruption avoidance in next-generation tokamaks using a safe reinforcement learning-based controller

Abstract

Achieving high particle density is desirable in fusion reactors because of its direct correlation to the fusion power. However, operational limits constrain the maximum achievable particle density. Several density control algorithms have been designed to inject particles using gas puffing and pellet injection to track carefully selected safe targets. However, a controller unaware of these operational limits may modulate the particle densities beyond the safe limits during transients caused by changing operating conditions. Designing plasma control algorithms that recognize these operational limits and ensure that the particle density remains within the safe operational space is desirable. This work focuses on the safe regulation of deuterium–tritium (DT) particle density. Under the assumption of quasi-neutrality, the total plasma density can be related to the electron density, which is constrained by the well-known Greenwald limit. To regulate the DT density within the safe operational space defined by Greenwald limit, a novel safe reinforcement learning-based controller is developed in this work. Such control-design approach can be critical in designing learning-based safe plasma control algorithms for next-generation tokamaks. The effectiveness of the controller is demonstrated through nonlinear simulations conducted over multiple test cases.