Intelligent Decision-Making Approach for Contingency Return Trajectory Based on Production Rule Base and Deep Learning

This article proposes an intelligent decision-making approach for a multibranch contingency return trajectory scheme, which is intended to satisfy the contingency requirement during the circumlunar flight phase in the manned lunar missions. First, based on the knowledge of orbital dynamics, a production rule base is constructed with the interval form. An expert system of contingency return trajectory is further designed to assist in the preliminary determination of contingency return schemes. Second, by adopting the fully connected neural network, a contingency return trajectory calculation model is established based on deep learning. Finally, combining the expert system and the calculation model, an intelligent decision-making approach is proposed to achieve rapid decision making of multibranch contingency return trajectories. The simulation shows that the calculation model can accurately generate the contingency return trajectory and has higher calculation efficiency than the traditional method. By using the proposed intelligent decision-making approach, a decision can be made quickly to determine a contingency return trajectory scheme and specific trajectory parameters can be obtained. The research results can provide an effective tool and important references for the decision making of a contingency return trajectory scheme in the future manned lunar missions.