@inproceedings{6344fdd9d3a642269320ddd67c8cbc74,
title = "The trajectory optimization of Space Maneuver Vehicle based-on dynamic neural network",
abstract = "In order to solve the problem of trajectory optimization of Space Maneuver Vehicle (SMV), a dynamic neural network method is introduced. Combined with neural network and Pontryagin's maximum principle, the method is able to approximate the optimal solution by neural network. At the same time, with the dynamic process, the problem of guessing covariates' initial value in traditional indirect method has been solved fairly well. In this work, the principle of the Dynamic Neural Network (DNN) optimal algorithm has been given and the optimization process has been described in detail. The simulation results indicated that using Dynamic Neural Network optimal algorithm can avoid guessing the covariates' initial value and satisfy the real-time requirements. Moreover, it has a higher accuracy solution.",
keywords = "Dynamic, Neural network, SMV vehicle, Trajectory optimization",
author = "Sainan Ren and Senchun Chai and Baihai Zhang and Fenxi Yao and Lingguo Cui",
note = "Publisher Copyright: {\textcopyright} 2017 Technical Committee on Control Theory, CAA.; 36th Chinese Control Conference, CCC 2017 ; Conference date: 26-07-2017 Through 28-07-2017",
year = "2017",
month = sep,
day = "7",
doi = "10.23919/ChiCC.2017.8027760",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "2633--2638",
editor = "Tao Liu and Qianchuan Zhao",
booktitle = "Proceedings of the 36th Chinese Control Conference, CCC 2017",
address = "United States",
}