3 papers
physics.plasm-ph2026
State-Space Model-Enabled Reinforcement Learning for Magnetic Configuration Controlon EXL-50U
Pei Guo, Zhengyuan Chen, Jianguo Chen +17
Accurate feedback control of the plasma current () and centroid position is essential for the stable operation of spherical torus (ST) plasmas. Conventional propor…
physics.plasm-ph2026
Reinforcement learning for vertical position control on the EXL-50U spherical tokamak
Lei Xing, Huicong Ma, Changquan Yu +14
Vertical position control is essential for sustaining high-performance operation in spherical tokamaks, where increased plasma elongation introduces stringent requirements on fast…
physics.plasm-ph2026
Advantage-level Aggregation Reinforcement Learning for X-point Target Magnetic Configuration Control in an EXL-50U Experiment-Calibrated Simulation Environment
Siqi Ding, Xuanhe Wang, Pei Guo +10
Managing divertor heat loads is a central challenge for compact, high-power tokamaks. To increase local flux expansion and decouple the dissipation volume from the core, EHL-2 adop…