AI 中文总结
针对聚变电站无内部线圈时高拉长比托卡马克垂直不稳定的问题，提出结合机器学习代理模型与约束二次规划的外部线圈反馈控制器，在ARC V3A仿真中83%的案例实现控制，界定了外部可控性边界。
AI 中文摘要
在本研究中，我们提出了一种基于模型的反馈控制器，该控制器仅使用外部极向场（PF）线圈，直接调节高拉长比双零托卡马克的垂直不稳定性增长率（$γ_{gr}$）。高拉长比可提升可达到的等离子体电流和聚变性能，但会使等离子体出现垂直不稳定性；而在聚变电站中，现有装置依赖的内部稳定线圈可能不会配备，仅能使用距离更远的外部回路。该控制器将非刚性、与剖面无关的垂直不稳定性度量的机器学习代理模型与约束二次规划相结合：代理模型提供实时$γ_{gr}$估计，并通过自动微分提供执行器灵敏度；二次规划则分配线圈电压，以跟踪目标增长率、维持双零偏滤器平衡并满足机电限制。我们在ARC V3A电站设计构型上测试了该方法，共开展24次闭环仿真，涵盖平衡变化、执行器退化和瞬态扰动等情况。在这些案例中，我们取得了83%的完全或边际成功（完全成功占50%，另有33%为边际成功），剩余17%的案例中失去控制；失效情况界定了外部控制的可控性边界（出现在最高增长率下）且处于执行器限制条件下。该控制器不直接调节边界形状：分界面几何形状由增长率和通量平衡控制间接决定，若要实现持续的场景演化，需要额外的独立形状控制层。
英文摘要
In this work, we propose a model-based feedback controller that regulates the vertical instability growth rate ($γ_{gr}$) of a high-elongation, double-null tokamak directly, using only out-vessel poloidal field (PF) coils. High elongation raises the achievable plasma current and fusion performance but makes the plasma vertically unstable, and in a fusion power plant the in-vessel coils that present devices rely on for stabilization may be absent, leaving only distant out-vessel circuits. The controller couples a machine learning surrogate of non-rigid, profile agnostic vertical instability metric to a constrained quadratic program: the surrogate supplies real-time $γ_{gr}$ estimates and, via automatic differentiation, the actuator sensitivities, while the program allocates coil voltages to track a target growth rate, maintain double-null divertor balance, and respect electromechanical limits. We tested this method on the ARC~V3A power plant design configuration across 24 closed-loop simulations spanning equilibrium variations, actuator degradations, and transient disturbances. We achieved full or marginal success in 83\% of these cases (full in 50\%, marginal in a further 33\%) and lose control in the remaining 17\%; the failures map the boundary of out-vessel controllability (occurring at the highest growth rates) and under actuator limits. The controller does not regulate boundary shape explicitly: separatrix geometry follows indirectly from growth rate and flux balance control and would require a separate shape control layer for sustained scenario evolution.
Comments17 pages
