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

Current successes in artificial intelligence domain have revitalized interest in neural networks and demonstrated their potential in solving spacecraft trajectory optimization prob-lems. This paper presents a data-free deep neural network (DNN) based trajectory optimization method for intercepting non-cooperative maneuvering spacecraft, in a continuous low-thrust scenario. Firstly, the problem is formulated as a standard con-strained optimization problem through differential game theory and minimax principle. Secondly, a new DNN is designed to in-tegrate 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 re-duces 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. Fi-nally, numerical results demonstrate the feasibility and efficiency of our proposed method.