面向变形滑翔飞行器的智能变形决策方法

王滔鋆; 孟凡一; 陈刚

doi:10.7652/xjtuxb202605021

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面向变形滑翔飞行器的智能变形决策方法

更新时间：2026-04-27

- 面向变形滑翔飞行器的智能变形决策方法
- Intelligent Deformation Decision-Making Method for Morphing Glide Vehicle
- 西安交通大学学报 2026年60卷第5期页码：217-225
- 作者机构：
  
  西安交通大学航天航空学院，710049，西安
- 作者简介：
  
  作者简介：王滔鋆（1998—），男，博士生；
  陈刚（通信作者），男，教授，博士生导师。
- 基金信息：
  
  国家自然科学基金资助项目(92371201)
- DOI：10.7652/xjtuxb202605021
  中图分类号： V448
- 收稿：2025-06-05，
  
  纸质出版：2026-05-10
- 稿件说明：
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王滔鋆, 孟凡一, 陈刚. 面向变形滑翔飞行器的智能变形决策方法[J]. 西安交通大学学报, 2026,60(5):217-225.

WANG Taojun, MENG Fanyi, CHEN Gang. Intelligent Deformation Decision-Making Method for Morphing Glide Vehicle[J]. Journal of Xi'an Jiaotong University, 2026, 60(5): 217-225.
王滔鋆, 孟凡一, 陈刚. 面向变形滑翔飞行器的智能变形决策方法[J]. 西安交通大学学报, 2026,60(5):217-225. DOI： 10.7652/xjtuxb202605021.

WANG Taojun, MENG Fanyi, CHEN Gang. Intelligent Deformation Decision-Making Method for Morphing Glide Vehicle[J]. Journal of Xi'an Jiaotong University, 2026, 60(5): 217-225. DOI： 10.7652/xjtuxb202605021.

摘要

为了充分发挥变形滑翔飞行器（MGV）相较于传统固定构型飞行器在航程、速度及环境适应性方面的显著优势，解决多物理场耦合下的最优变形构型决策难题，提出一种基于深度强化学习的智能变形决策方法。首先，利用深度神经网络构建翼面变形与气动性能的代理模型，实现了从变形动作到气动数据的端到端快速映射；然后，结合飞行器纵向运动模型建立强化学习框架，并通过算法性能对比，选用近端策略优化（PPO）算法构建智能决策模型，实现了气动/变形/弹道交叉耦合下的自主策略学习；最后，在训练范围外的拉偏环境中进行全流程仿真实验验证。结果表明：所提方法能够综合气动、变形、弹道耦合影响进行智能变形决策，使MGV终端射程增加7.304%，并能有效维持较高的升阻比飞行状态；同时，拉偏测试证明了该方法在不确定环境下依然具备良好的可行性与泛化能力。

Abstract

To fully leverage the significant advantages of morphing glide vehicles (MGVs) over traditional fixed-configuration aircraft in terms of range

speed

and environmental adaptability

and to address the challenge of optimal morphing configuration decision-making under multiphysics coupling

an intelligent morphing decision-making method based on deep reinforcement learning is proposed. First

a deep neural network was used to construct a surrogate model for wing surface morphing and aerodynamic performance

achieving end-to-end rapid mapping from morphing actions to aerodynamic data. Then

a reinforcement learning framework was established by integrating the longitudinal motion model of the vehicle. Through comparative analysis of algorithm performance

the proximal policy optimization (PPO) algorithm was selected to construct the intelligent decision-making model

enabling autonomous policy learning under aerodynamic/morphing/traj ectory cross-coupling. Finally

full-process simulation experiments were conducted in perturbed environments beyond the training scope for validation. The results show that the proposed method can intelligently make morphing decisions by comprehensively considering the coupling effects of aerodynamics

morphing

and traj ectory

increasing the terminal range of the MGV by 7.304% and effectively maintaining a high lift-to-drag ratio flight state. Additionally

perturbation tests demonstrate that the method remains feasible and exhibits strong generalization capabilities in uncertain environments.

关键词

Keywords

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