Autonomous Entry Guidance Method for Online Encounters with Multiple No-fly Zones
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.
Entry guidanceMulti-no-fly zone avoidanceReinforcement learningFuzzy controlOnline autonomous decision-making
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