Receding horizon optimization for spacecraft pursuit-evasion strategy in rendezvous

Given the influence of uncertainty such as measurement errors in the process of spacecraft free-time orbital pursuit-evasion game for rendezvous, a high-efficiency strategy based on receding horizon optimization was proposed as a solution method. The saddle point control strategy of the game was derived according to differential games, and the equivalent transformation of the problem was carried out. By solving open-loop saddle point strategy off-line in advance, the initial states of the problem and the corresponding solutions were taken as samples for neural network training, and the trained network structure can quickly obtain the approximate solution of the corresponding problem. In order to better deal with the measurement noise in the game environment, a receding horizon optimization framework was designed based on the neural network structure. By periodically solving the problem, the rendezvous of the pursuer and evader was finally realized. Numerical simulation shows that the proposed strategy can effectively deal with the uncertainty of measurement noise, and compared with the existing strategy in the literature, the calculation time can be reduced from minutes to several seconds.