北京航空航天大学学报 ›› 2022, Vol. 48 ›› Issue (5): 910-919.doi: 10.13700/j.bh.1001-5965.2020.0686

• 论文 • 上一篇    下一篇

基于DDPG算法的变体飞行器自主变形决策

桑晨1, 郭杰1, 唐胜景1, 王肖2, 王子瑶1   

  1. 1. 北京理工大学 宇航学院, 北京 100081;
    2. 中国运载火箭技术研究院, 北京 100076
  • 收稿日期:2020-12-08 发布日期:2022-05-30
  • 通讯作者: 郭杰 E-mail:guojie1981@bit.edu.cn

Autonomous deformation decision making of morphing aircraft based on DDPG algorithm

SANG Chen1, GUO Jie1, TANG Shengjing1, WANG Xiao2, WANG Ziyao1   

  1. 1. School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China;
    2. China Academy of Launch Vehicle Technology, Beijing 100076, China
  • Received:2020-12-08 Published:2022-05-30

摘要: 针对变体飞行器的自主变形决策问题,提出了一种基于深度确定性策略梯度(DDPG)算法的智能二维变形决策方法。以可同时变展长及后掠角的飞行器为研究对象,利用DATCOM计算气动数据,并通过分析获得变形量与气动特性之间关系;基于给定的展长和后掠角变形动力学方程,设计DDPG算法学习步骤;针对对称和不对称变形条件下的变形策略进行学习训练。仿真结果表明:所提算法可以快速收敛,变形误差保持在3%以内,训练好的神经网络提高了变体飞行器对不同飞行任务的适应性,可以在不同的飞行环境中获得最佳的飞行性能。

关键词: 变体飞行器, 自主变形决策, 深度强化学习, 深度确定性策略梯度(DDPG)算法, 动力学分析

Abstract: An intelligent 2D deformation decision method based on deep deterministic policy gradient (DDPG) algorithm is proposed for the autonomous deformation decision making of morphing aircraft. The vehicle that can change at the same time the span length and sweepback is taken as the research object, DATCOM is used to calculate the aerodynamic data, and through the analysis, the relation between deformation and aerodynamic characteristics is obtained. DDPG algorithm learning steps are designed based on the given span length and sweepback deformation dynamics equation. The deformation strategy under the condition of symmetrical and asymmetrical deformation is learned and used to train. The simulation results show that the proposed algorithm can achieve fast convergence, and the deformation error is kept within 3%. The trained neural network improves the adaptability of the morphing aircraft to different flight missions, and the optimal flight performance can be obtained in different flight environments.

Key words: morphing aircraft, autonomous deformation decision making, deep reinforcement learning, deep deterministic policy gradient (DDPG) algorithm, kinetic analysis

中图分类号: 


版权所有 © 《北京航空航天大学学报》编辑部
通讯地址:北京市海淀区学院路37号 北京航空航天大学学报编辑部 邮编:100191 E-mail:jbuaa@buaa.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发