A DNN based trajectory optimization method for intercepting non-cooperative maneuvering spacecraft  

作　　者：YANG Fuyunxiang YANG Leping ZHU Yanwei ZENG Xin 

机构地区：[1]College of Aerospace Science and Engineering,National University of Defense Technology,Changsha 410073,China

出　　处：《Journal of Systems Engineering and Electronics》2022年第2期438-446,共9页系统工程与电子技术（英文版）

基　　金：supported by the National Defense Science and Technology Innovation (18-163-15-Lz-001-004-13)。

摘　　要：Current successes in artificial intelligence domain have revitalized interest in neural networks and demonstrated their potential in solving spacecraft trajectory optimization problems. This paper presents a data-free deep neural network(DNN) based trajectory optimization method for intercepting noncooperative maneuvering spacecraft, in a continuous low-thrust scenario. Firstly, the problem is formulated as a standard constrained optimization problem through differential game theory and minimax principle. Secondly, a new DNN is designed to integrate interception dynamic model into the network and involve it in the process of gradient descent, which makes the network endowed with the knowledge of physical constraints and reduces the learning burden of the network. Thus, a DNN based method is proposed, which completely eliminates the demand of training datasets and improves the generalization capacity. Finally, numerical results demonstrate the feasibility and efficiency of our proposed method.

关 键 词：non-cooperative maneuvering spacecraft neural network differential game trajectory optimization 

分 类 号：V448.2[航空宇航科学与技术—飞行器设计]