The modeling and control of the plasma equilibrium response is still one of the more important research areas in tokamak discharge experiments. Although theoretically, first principles can predict the plasma instability, how to build a physical model for accurate prediction is still a challenging problem. Therefore, a deep learning method is proposed to model the plasma vertical displacement system in the HL-2A tokamak experiment, whose method expands the modeling strategy for tokamak plasma control systems. Through the training of a large number of high-dimensional experimental data, the obtained deep neural network model in this paper has a higher precision prediction ability. Additionally, to illustrate the significance of the predictive model in controller design, a data-driven adaptive control algorithm is proposed to replace the traditional proportional-integral-derivative control algorithm for controlling the vertical displacement of plasma. The simulation results showed ...

中文翻译：

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基于深度学习的HL-2A等离子垂直位移控制系统建模及其控制器设计

等离子体平衡响应的建模和控制仍然是托卡马克放电实验中更重要的研究领域之一。尽管从理论上讲，第一原理可以预测等离子体的不稳定性，但是如何建立物理模型以进行准确的预测仍然是一个具有挑战性的问题。因此，在HL-2A托卡马克实验中，提出了一种对等离子体垂直位移系统进行建模的深度学习方法，该方法扩展了托卡马克等离子体控制系统的建模策略。通过训练大量的高维实验数据，本文获得的深度神经网络模型具有较高的精度预测能力。另外，为了说明预测模型在控制器设计中的重要性，提出了一种数据驱动的自适应控制算法，以代替传统的比例积分微分控制算法来控制等离子体的垂直位移。仿真结果表明...