论文标题
使用深神经网络的J2扰动兰伯特问题的快速求解器
Fast solver for J2-perturbed Lambert problem using deep neural network
论文作者
论文摘要
本文为J2扰动的兰伯特问题提供了一个新颖而快速的解决者。求解器由智能初始猜测发生器和差分校正过程组成。智能的初始猜测发生器是一个深层神经网络，该网络经过训练，可以纠正来自未受干扰的兰伯特问题的解决方案的初始速度向量。差分校正模块采用初始猜测，并使用正向射击过程进一步更新初始速度并准确地满足终端条件。分析了八种样本形式，并比较找到在J2扰动的兰伯特问题上训练神经网络的最佳形式。这种新颖方法的准确性和性能将在代表性的测试案例上证明：木星系统中多革命J2扰动的兰伯特问题的解决方案。我们将比较拟议方法的性能与经典标准拍摄方法和基于同型扰动的兰伯特算法的性能。可以证明，对于可比的准确性，所提出的方法明显快于其他两种。
This paper presents a novel and fast solver for the J2-perturbed Lambert problem. The solver consists of an intelligent initial guess generator combined with a differential correction procedure. The intelligent initial guess generator is a deep neural network that is trained to correct the initial velocity vector coming from the solution of the unperturbed Lambert problem. The differential correction module takes the initial guess and uses a forward shooting procedure to further update the initial velocity and exactly meet the terminal conditions. Eight sample forms are analyzed and compared to find the optimum form to train the neural network on the J2-perturbed Lambert problem. The accuracy and performance of this novel approach will be demonstrated on a representative test case: the solution of a multi-revolution J2-perturbed Lambert problem in the Jupiter system. We will compare the performance of the proposed approach against a classical standard shooting method and a homotopy-based perturbed Lambert algorithm. It will be shown that, for a comparable level of accuracy, the proposed method is significantly faster than the other two.
