多禁飞区在线遭遇的自主规避再入制导方法

Considering the need for avoiding multiple unknown threats during the entry process of hypersonic glide vehicles，an autonomous entry guidance method is proposed for online encountering with multiple no-fly zones. The problem of sequentially avoiding multiple no-fly zones encountered in flight is treated as a sequential decision-making problem. A solution based on reinforcement learning is designed to enhance the autonomous capability of the vehicle. The Markov decision process for the no-fly zone avoidance problem is formulated，taking into account both the generalization capability and training efficiency of the reinforcement learning agent. Furthermore，a multi-agent coordination decision-making method is developed using a fuzzy control strategy. This method assigns a heading decision-making agent to each online-detected no-fly zone，making independent heading decisions. The method conducts real-time environmental assessments to evaluate the threat level of each no-fly zone and coordinates the generation of heading commands. Theoretical analysis and numerical simulations demonstrate that the proposed method enables effective avoidance of multiple no-fly zones encountered in flight，satisfying both terminal and process constraints. The method exhibits robustness and generalization capabilities，showcasing its effectiveness in diverse scenarios.