基于深度神经网络的HL-2A等离子体水平位移研究

基于门控循环单元(GRU)的神经网络，构建预测模型的网络拓扑结构，训练和测试了HL-2A装置等离子体水平位移系统响应模型。测试结果显示了该模型对43%的样本数据的拟合度超过80%。把该网络模型作为被控对象，使用基于径向基函数(RBF)神经网络的模型参考自适应控制(MRAC)算法，设计了一个HL-2A等离子体水平位移的MRAC系统。仿真结果显示，该控制系统的输出响应能快速地跟踪各种输入参考信号，控制器能够较好地控制等离子体的水平位移并具有强的抗扰动能力。

Modeling and control of HL-2A plasma horizontal displacement based on neural networks

Based on the gated recurrent unit (GRU), the network topology of the prediction model is built, to train and test the response model of the HL-2A plasma horizontal displacement system. The test results show that the fitting degree of 43% of the sample data exceeds 0.8. Using the network model as the controlled object, an HL-2A plasma horizontal displacement MRAC system is designed with a model reference adaptive control (MRAC) algorithm based on radial basis function (RBF) neural network. The simulation results show that the output response of the control system can quickly track various input reference signals. The controller can control the horizontal displacement of the plasma and has strong anti-disturbance capability.