An optimal guidance method for free-time orbital pursuit-evasion game

With the development of space rendezvous and proximity operations (RPO) in recent years, the scenarios with non-cooperative spacecraft are attracting the attention of more and more researchers. A method based on the costate normalization technique and deep neural networks is presented to generate the optimal guidance law for free-time orbital pursuit-evasion game. Firstly, the 24-dimensional problem given by differential game theory is transformed into a three-parameter optimization problem through the dimension-reduction method which guarantees the uniqueness of solution for the specific scenario. Secondly, a close-loop interactive mechanism involving feedback is introduced to deep neural networks for generating precise initial solution. Thus the optimal guidance law is obtained efficiently and stably with the application of optimization algorithm initialed by the deep neural networks. Finally, the results of the comparison with another two methods and Monte Carlo simulation demonstrate the efficiency and robustness of the proposed optimal guidance method.