基于深度强化学习的尾旋改出技术

Aircraft Spin Recovery Technique Based on Deep Reinforcement Learning

本文搭建了飞机仿真环境,基于近端策略优化(PPO)算法建立了尾旋改出算法测试模型,设计了基准版单阶段、基准版双阶段、加深版单阶段、加深版双阶段四种网络结构,用于探究网络结构和改出阶段对尾旋改出效果的影响,设置了鲁棒性测试试验,从时延、误差和高度等方面进行了算法测试和结果分析。

This paper builds an aircraft simulation environment,and establishes a test model of an automated spin recovery algorithm based on proximal policy optimization(PPO)algorithm.Four kinds of network structures are designed,that are basis single stage,basis double stage,deep single stage and deep double stage,to explore the influence of network structure and recovery stage on spin recovery effect.A robustness test experiment is set up,and the algorithm is tested and the results are analyzed from the aspects of delay,error and height.