计算机科学 ›› 2025, Vol. 52 ›› Issue (6A): 240600068-7.doi: 10.11896/jsjkx.240600068
徐丹, 王江涛
XU Dan, WANG Jiangtao
摘要: 智能变形飞行器是一类能够根据飞行任务与环境变化适时、自主地改变结构外形,以不同气动布局满足不同飞行阶段任务需求的新一代飞行器,被认为是最有可能带来未来航空航天飞行器技术变革的发展趋势之一。但较大的结构变形使其难以建立准确的数学模型,为此提出利用无模型的强化学习(Reinforcement Learning,RL)算法,通过交互学习实现轨迹优化的自主决策。以大空域高速飞行的智能变形飞行器为研究对象,针对其难以提前获取充足的变形飞行试验数据导致难以预测不同飞行状态下的最优气动外形的技术问题,提出了一种基于RL网络模型的变形决策优化设计方案。所提方案使得飞行器在飞行过程中可以根据实时情况自主完成决策变形,达到提升气动性能和优化飞行轨迹的目的。
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