The invention discloses a method for estimating low-thrust intersection burnup by using a neural network. The method comprises the following steps: firstly, obtaining a training data set in which initial and target orbits, intersection duration and burnup are in one-to-one correspondence; secondly, two neural networks are built, one neural network is used for building an agent model for the realizability of the small-thrust intersection trajectory, and the other neural network is used for building an agent model for small-thrust intersection burnup calculation; the method comprises the following steps: training a neural network by using sampling data, taking any input initial orbit and target orbit as input layer data, firstly substituting the input layer data into a first neural network to obtain output layer data, namely the feasibility of low-thrust intersection, and if the orbit transfer is feasible, continuously substituting the input layer data into a second neural network to obtain output data, namely the feasibility of low-thrust intersection. The increment is the increment of the low-thrust rendezvous speed. According to the method, the problems of large calculation amount, low convergence performance and low approximation precision of a low-thrust trajectory optimization method in the prior art are solved.
本发明公开了一种使用神经网络估计小推力交会燃耗量的方法,首先获得初始和目标轨道、交会时长与燃耗量一一对应的训练数据集;然后搭建两个神经网络,其中一个用以建立小推力交会轨迹的可实现性的代理模型,另一个用以建立小推力交会燃耗量计算的代理模型;利用采样数据对神经网络进行训练,最后根据输入的任意初始轨道和目标轨道,作为输入层数据,首先代入第一个神经网络获得输出层数据,即为小推力交会的可行性,若该轨道转移可行,则继续代入第二个神经网络获得输出数据,即为小推力交会速度增量。本发明解决了现有技术中存在的小推力轨迹优化方法计算量大、收敛性能低且近似精度低的问题。
Method for estimating low-thrust intersection fuel consumption by using neural network
使用神经网络估计小推力交会燃耗量的方法
2023-06-13
Patent
Electronic Resource
Chinese
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