Ion temperature gradient control using reinforcement learning technique
Ion temperature gradient control using reinforcement learning technique
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DOI：
10.1088/1741-4326/abe68d
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发表时间：
2021-02
期刊：
影响因子：
3.3
通讯作者：
Takuma Wakatsuki;Takahiro Suzuki;Naoyuki Oyama;Nobuhiko Hayashi
中科院分区：
文献类型：
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作者：
Takuma Wakatsuki;Takahiro Suzuki;Naoyuki Oyama;Nobuhiko Hayashi
具有内输运势垒(ITB)的等离子体由于具有高的约束质量和高的自举电流分数，是稳态托卡马克反应堆所需要的。然而，局部压力梯度趋于陡峭，等离子体经常变得不稳定。本文利用强化学习技术开发了一种基于中性束注入(NBI)的离子温度梯度控制系统。离子温度梯度对NBI的响应特性是非线性的，对实验条件敏感，这给开发鲁棒控制系统带来了困难。我们的控制系统针对具有广泛ITB强度的等离子体进行了培训。使用强化学习技术，该系统在主题主持的综合运输模拟中通过数千次迭代试验获得了稳健的控制特征。控制系统由神经网络(NNS)组成，其输入变量为离子温度梯度、当前NBI功率和前几个控制时间步长的NBI功率。训练后的系统可以确定适合从输入变量推断出的响应特性的控制输出。基于JT-60U的两个不同ITB强度的实验等离子体，利用等离子体模型对训练好的控制系统进行了主题仿真。结果表明，对于两种等离子体，离子温度梯度都可以得到适当的控制，这支持了该系统适用于实际实验的预期。
Plasma with an internal transport barrier (ITB) is desirable for a steady-state tokamak reactor because of its high confinement quality and high bootstrap current fraction. However, the local pressure gradient tends to be steep and the plasma often becomes unstable. In this study, an ion temperature gradient control system based on neutral beam injection (NBI) is developed using the reinforcement learning technique. The response characteristics of an ion temperature gradient to NBI are non-linear and sensitive to experimental conditions, which makes it difficult to develop a robust control system. Our control system is trained for plasmas with a wide range of ITB strengths. Using the reinforcement learning technique, the system acquires a robust control feature through several thousand iterations of trial and error in an integrated transport simulation hosted by TOPICS. The control system is composed of neural networks (NNs) whose input variables are the ion temperature gradient, the current NBI power, and the NBI powers for several previous control time steps. The trained system can determine a control output which is suitable for the response characteristics inferred from the input variables. The trained control system is tested in the TOPICS simulation using plasma models based on two experimental plasmas of JT-60U with different ITB strengths. It is shown that the ion temperature gradient can be appropriately controlled for both plasmas, which supports the expectation that this system is applicable to real experiments.