基于BP神经网络的纳卫星轨道温度预测

Temperature prediction for nano satellite on orbit based on BP neural network

为了实现纳卫星在轨温度的预测,在对纳卫星热系统动态特性模型分析的基础上,建立BP神经网络预测模型实现纳卫星在轨温度的预测.通过分析纳卫星热系统动态特性模型,得到用于BP神经网络预测模型的输入、输出变量以及训练神经网络所需的数据样本.BP神经网络预测模型分别以纳卫星外壳、辐射器、舱内仪器的热流及温度值为神经网络输入、输出,预测纳卫星10 s后的轨道温度.经验证,神经网络预测模型预测结果与纳卫星实际轨道温度吻合较好,表明神经网络预测模型是快捷有效的.

In order to realize the temperature prediction for nano satellite on orbit, a basic thermal environment model of nano satellite was established and analyzed. A BP neural network was used to predict the temperature of nano satellite based on it. After the analysis of the thermal environment model, the thermal flow of satellite＇s crust, radiater and equipments in cabin was got and used as the inputs of the BP neural network, their temperatures were used as the outputs and data sample of BP neural network was got and used to train BP neural network. The BP neural network which was trained is the temperature prediction model of nano satellite and used to predict the temperature of the satellite on orbit after 10 s. Results fit the actual temperature of nano satellite on orbit well, and show that the temperature prediction model of neural network can predict the temperature of nano satellite accurately and rapidly.