基于代理模型的卫星编队重构最短距离建模

针对编队卫星成员数量较多时，编队重构规划考虑碰撞规避会带来的巨大的计算开销。为降低计算开销和提升优化效率，基于CW方程和双脉冲轨道机动策略，建立了能够快速预测编队卫星重构过程最短距离的多种代理模型，并基于三种大小不同的训练集，从模型精度和效率两方面进行了对比。结果表明，克里金（KRG）模型在各种代理模型中精度最高，而且随着训练量的增加，克里金和人工神经网络（ANN）模型的性能得到了明显改善，模型精度有一定的保证。研究还发现，尽管KRG模型预测时间高于其他代理模型，但和真实模型相比，其耗时仍然很短，因此可用于提高考虑避碰约束时卫星编队重构轨迹优化的效率。

Shortest Distance Modeling of Satellite Formation Reconfiguration Based on Surrogate Model

When the number of formation satellites is large, the formation reconfiguration planning brings the huge computational cost considering collision avoidance. In order to reduce the computational overhead and improve the optimization efficiency, based on the CW equation and the dual pulse orbit maneuver strategy, a variety of surrogate models that can quickly predict the shortest distance in the formation satellite reconfiguration process are established. Based on three training sets of different sizes, the model accuracy and efficiency are compared. The result shows that Kriging (KRG) model has the highest accuracy among various surrogate models. Moreover, with the increase of training, the performance of Kriging and artificial neural network (ANN) models has been significantly improved, and the accuracy of the model is guaranteed. It is also found that although the prediction time of KRG model is higher than that of other surrogate models, its time-consuming is still very short compared with the real model, so it can be used to improve the efficiency of satellite formation reconfiguration trajectory optimization considering collision avoidance constraints.