基于平流层风场预测的浮空器轨迹控制

平流层风场环境对浮空器设计和轨迹控制具有重要影响。针对平流层风场建模，以长沙地区2005—2010年的风场数据为例，首先采用本征正交分解（POD）方法对风场数据进行降阶处理；然后分别采用Fourier级数与BP神经网络算法对平流层风场进行预测，并对2种模型的预测精度进行比较分析；最后通过建立临近空间浮空器的动力学模型和高度调控模型，分析2种风场预测模型对浮空器轨迹控制的影响。研究结果表明，相对于Fourier预测模型，基于BP神经网络预测模型的预测精度更高，可信度更强，能够更好地为浮空器飞行轨迹控制提供参考价值。 

Trajectory control of aerostat based on prediction of stratospheric wind field

The stratospheric wind environment has an important influence on the aerostat design and trajectory control. Taking the wind field data from 2005 to 2010 in Changsha as an example, this paper firstly uses the proper orthogonal decomposition (POD) method to reduce order of the wind field data, and then uses the Fourier series and BP neural network algorithm to predict the stratospheric wind field. The prediction accuracy of the two models is compared and analyzed. Finally, the dynamic model and height control model of the near-space aerostat are established, and the influence of the two wind field prediction models on the trajectory control of the aerostat is analyzed. The research results show that the prediction model based on the BP neural network is more accurate and more reliable than the Fourier prediction model, and it can provide a better reference value for the flight trajectory control of the aerostat.