基于深度神经网络的航天器反交会逃逸方法

Anti-Rendezvous Evasion of Spacecraft Based on Deep Neural Networks

针对空间近距离非合作交会,提出一种基于深度神经网络(DNN)的航天器智能反交会逃逸方法.首先建立了描述逃逸脉冲优化的双层数学规划(MP)问题模型;然后,选定神经网络的输入与输出,根据前述建立的模型,通过粒子群优化(PSO)算法计算不同相对状态下的最优逃逸脉冲,构建样本集;最后,设计神经网络并进行训练,通过比较学习效果合理选择网络的超参数.仿真结果表明,充分训练后的深度神经网络可以高精度地快速生成逃逸脉冲,并具有较好的泛化性能,可满足轨道博弈中对逃逸机动计算快速性和实时性的要求,为反交会逃逸提供了一种智能化手段.

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.