Abstract: An intelligent framework based on deep neural networks (DNNs) is proposed to achieve the evasive impulse for spacecraft against close proximity non cooperative rendezvous. First, a double layer mathematical programming (MP) model is established to describe the evasive impulse optimization problem. Then, the input and output parameters of DNNs are carefully selected. Based on the double layer MP model, a dataset is established by using the particle swarm optimization (PSO) algorithm to obtain optimal evasive impulses under different relative states. Finally, DNNs are designed and trained, and the hyper parameters of networks are elaborately chosen by evaluating the learning performances. Simulation results indicate that well trained DNNs can calculate optimal evasive impulses with a high precision and a fast speed. Our approach can promote the intelligentization of on orbit evasion and efficiently improve the survivability of spacecraft in the orbital game.

Key words: non cooperative rendezvous, evasive impulse, mathematical programming, deep neural network (DNN), intelligentization