计算机科学 ›› 2025, Vol. 52 ›› Issue (6A): 240600068-7.doi: 10.11896/jsjkx.240600068

• 人工智能 • 上一篇    下一篇

智能变形飞行器自主决策轨迹优化方法设计

徐丹, 王江涛   

  1. 西安建筑科技大学信息与控制工程学院 西安 710055
  • 出版日期:2025-06-16 发布日期:2025-06-12
  • 通讯作者: 徐丹(xdan@xauat.edu.cn)
  • 基金资助:
    陕西省自然科学基础研究计划(2024JC-YBQN-0458)

Design of Autonomous Decision for Trajectory Optimization of Intelligent Morphing Aircraft

XU Dan, WANG Jiangtao   

  1. College of Information and Control Engineering,Xi’an University of Architecture and Technology,Xi’an 710055,China
  • Online:2025-06-16 Published:2025-06-12
  • About author:XU Dan,born in 1992,Ph.D,lecturer,is a member of CCF(No.U4531M).Her main research interests include RL intelligent decision and autonomous control,intelligent cluster,and intelligent collaborative algorithm,et al.
  • Supported by:
    Shaanxi Province Natural Science Basic Research Program(2024JC-YBQN-0458).

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。

关键词: 变形飞行器, 强化学习, 智能决策, 最优气动外形优化, 轨迹优化

Abstract: Intelligent morphing aircraft is a new generation of aircraft that can timely and autonomously change the structural shape according to the changes of flight mission and environment,and meet the requirements of different flight stages with diffe-rent aerodynamic layouts.It is considered as one of the development trends that is most likely to bring about the technological change of aerospace vehicles in the future.However,large structural deformation makes it difficult to establish an accurate mathematical model.Therefore,it is proposed to use the model-free reinforcement learning(RL) algorithm to realize the autonomous decision-making of trajectory optimization through interactive learning.This paper takes intelligent morphing aircraft flying at high speed in large airspace as the research object,aiming at the technical problems that it is difficult to obtain sufficient defor-mable flight test data in advance,which makes it difficult to predict the optimal aerodynamic shape under different flight states,a deformable decision optimization design scheme based on RL network model is proposed.The deformations can be made independently according to real-time conditions during flight,so as to achieve the mission objectives of improving aerodynamic performance and optimizing flight trajectory.

Key words: Morphing aircraft, Reinforcement learning, Intelligent decision-making, optimal aerodynamic shape optimization, trajectory optimization

中图分类号: 

  • TP391.9
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