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Identification and control of plasma vertical position using neural network in Damavand tokamak

机译:用神经网络识别和控制Damavand托卡马克中的血浆垂直位置

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In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg–Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.
机译:在这项工作中,引入了非线性模型来确定Damavand托卡马克中等离子柱的垂直位置。使用该模型作为模拟器,已经设计了非线性神经网络控制器。在第一阶段,对Damavand tokamak的电子驱动和传感电路进行了修改。这些电路可以控制真空容器内等离子柱的垂直位置。由于等离子体的垂直位置是不稳定的参数,因此执行直接闭环系统识别算法。在第二阶段,基于多层感知器(MLP)神经网络(NN)结构,识别血浆垂直位置的非线性模型。模拟器参数的估计已使用Levenberg-Marquardt梯度下降优化技术通过反向传播误差算法进行。通过在类似条件下使用模拟器和实际工厂对整个闭环系统进行仿真来验证该模型。最后阶段,针对仿真器模型设计了MLP神经网络控制器。在最后一步中,执行在线培训以调整控制器参数。仿真结果证明了将NN控制器用于实际工厂是合理的。

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