南京航空航天大学学报（英文版）2023，Vol.40Issue(5)：578-594,17.DOI:10.16356/j.1005-1120.2023.05.007
基于深度神经网络的变比冲小推力交会实时最优控制
Real-Time Optimal Control for Variable-Specific-Impulse Low-Thrust Rendezvous via Deep Neural Networks
摘要
Abstract
This paper presents a real-time control method based on deep neural networks(DNNs)for the fuel-optimal rendezvous problem.A backward generation optimal examples method for the fuel-optimal rendezvous problem is proposed,which iterates through the dichotomy method based on the existing backward generation idea while satisfying the two integration cutoff conditions of the backward integration.We construct a DNNs structure suitable for the variable-specific-impulse model and divide the output control of networks into the thrust output and the specific impulse output.For the specific impulse output,a method is proposed that learns the optimal specific impulse first and then limits it according to its actual upper and lower limits.We propose the enhanced fault-tolerant deep neural networks(EFT-DNNs)to improve the robustness when approaching rendezvous.The effectiveness and efficiency of the proposed method are verified by simulations of the Earth-Apophis asteroid and Earth-Mars missions.关键词
轨迹优化/变比冲/燃料最优控制/间接法/深度神经网络Key words
trajectory optimization/variable specific impulse/fuel-optimal control/indirect method/deep neural networks(DNNs)分类
航空航天引用本文复制引用
刘宇航,杨洪伟..基于深度神经网络的变比冲小推力交会实时最优控制[J].南京航空航天大学学报（英文版）,2023,40(5):578-594,17.基金项目
This work was supported by the Na-tional Natural Science Foundation of China(No.12102177)and the Natural Science Foundation of Jiangsu Province(No.BK20220130). （No.12102177）
