Artificial Intelligence-assisted control of Alfvén Eigenmodes improves plasma stability in the DIII-D tokamak*

Alfvén Eigenmodes (AEs) are an important challenge in the development of sustainable fusion energy, as they can significantly impact the confinement and stability of energetic particles in fusion plasmas. This work presents a novel approach for real-time feedback control of AEs using multiple neutral beams at the DIII-D National Fusion Facility. Building upon prior predictive models that utilize electron cyclotron emission diagnostics and neutron deficit analysis, this study introduces a new feedback control system that actively modulates individual neutral beams in real-time to suppress AE activity [1,2]. The system dynamically adjusts the beam power of each beam based on real-time measurements of AE signatures. Real-time integration of Reservoir Computing Networks (RCN) predicted the neutron rate, enabling the controller to follow Reversed Shear Alfvén Eigenmode (RSAE) activity within the constraints of neutral beam injection (NBI) programming. This work also discusses the implementation of alternate actuators, such as gas puffing (density control) to enhance the AE mitigation methods. Experimental results demonstrate the effectiveness of this multi-beam feedback approach, marking a milestone in the understanding and mitigation of AEs, and paving the way for improved stability and confinement in future fusion reactors.