Intelligent Deformation Decision-Making Method for Morphing Glide Vehicle

WANG Taojun; MENG Fanyi; CHEN Gang

doi:10.7652/xjtuxb202605021

您当前的位置：

首页 >

文章列表页 >

Intelligent Deformation Decision-Making Method for Morphing Glide Vehicle

更新时间：2026-04-27

- Intelligent Deformation Decision-Making Method for Morphing Glide Vehicle
- Journal of Xi'an Jiaotong University Vol. 60, Issue 5, Pages: 217-225(2026)
- 作者机构：
  
  西安交通大学航天航空学院，710049，西安
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202605021
  CLC： V448
- Received：05 June 2025，
  
  Published：10 May 2026
- 稿件说明：
移动端阅览
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：

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

references

SHI Rongqi，SONG Jianmei.Dynamics and control for an in-plane morphing wing [J].Aircraft Engineering and Aerospace Technology，2013，85（1）：24-31.

王青，王通，董朝阳，等.变体飞行器链式平滑切换控制[J].控制理论与应用，2015，32（7）：949-954.

WANG Qing，WANG Tong，DONG Chaoyang，et al. Chained smooth switching control for morphing aircraft[J]. Control Theory &Applications，2015，32（7）：949-954.

郭雷，王恩美，卢昊，等.仿生变构型飞行器智能控制技术：进展与展望[J].上海航天（中英文），2022，39（4）：25-32.

GUO Lei，WANG Enmei，LU Hao，et al.Intelligent control technologies for bio-inspired morphing flight vehicles：progress and prospect[J].Aerospace Shanghai（Chinese & English），2022，39（4）：25-32.

王子健，张书宇，侯明哲.基于在线参数辨识的变体飞行器控制[J].兵器装备工程学报，2022，43（10）：60-65.

WANG ZI Jian，ZHANG Shuyu，HOU Mingzhe. Morphing aircraft control based on on-line parameter identification[J].Journal of Ordnance Equipment Engineering，2022，43（10）：60-65.

BAO Weimin，LÜ Xiaozhou，BAI Guanghui，et al. Exploration on the frontiers of flexible and deformable cross-domain intelligent flight [J].Aerospace China，2021，22（1）：5-11.

PALACIOS R，MURUA J，COOK R.Structural and aerodynamic models in nonlinear flight dynamics of very flexible aircraft [J].AIAA Journal，2010，48（11）：2648-2659.

郭东，徐敏，陈士橹.弹性飞行器飞行动力学建模研究[J].空气动力学学报，2013，31（4）：413-419.

GUO Dong，XU Min，CHEN Shilu.Research on flight dynamic modeling of highly flexible aircrafts[J].Acta Aerodynamica Sinica，2013，31（4）：413-419.

CHEN Xiaoyu，LI Chunna，GONG Chunlin，et al.A study of morphing aircraft on morphing rules along trajectory [J].Chinese Journal of Aeronautics，2021，34（7）：232-243.

YUE Ting，ZHANG Xiangyang，WANG Lixin，et al. Flight dynamic modeling and control for a telescopic wing morphing aircraft via asymmetric wing morphing [J]. Aerospace Science and Technology，2017，70：328-338.

YUE Ting，WANG Lixin，AI Junqiang.Longitudinal linear parameter varying modeling and simulation of morphing aircraft [J].Journal of Aircraft，2013，50（6）：1673-1681.

GONG Ligang，WANG Qing，HU Changhua，et al. Switching control of morphing aircraft based on Qlearning [J].Chinese Journal of Aeronautics，2020，33（2）：672-687.

BAO Cunyu，WANG Peng，TANG Guojian.Integrated method of guidance，control and morphing for hypersonic morphing vehicle in glide phase [J].Chinese Journal of Aeronautics，2021，34（5）：535-553.

SILVER D，LEVER G，HEESS N，et al.Deterministic policy gradient algorithms [C]//Proceedings of the 31st International Conference on International Conference on Machine Learning.Cambridge，UK：JMLR，2014：I-387-I-395.

SCHULMAN J，WOLSKI F，DHARIWAL P，et al. Proximal policy optimization algorithms [EB/OL].（2017-08-28）[2025-06-30].https：//arxiv.org/abs/1707.06347.

LILLICRAP T，HUNT J，PRITZEL A，et al.Continuous control with deep reinforcement learning [EB/OL].（2015-09-09）[2025-06-01].https：//arxiv.org/abs/1509.02971.

BHATNAGAR S，SUTTON R S，GHAVAMZA-DEH M，et al.Natural actor-critic algorithms [J]. Automatica，2009，45（11）：2471-2482.

CHEN Wenxue，GAO Changsheng，JING Wuxing. Proximal policy optimization guidance algorithm for intercepting near-space maneuvering targets [J].Aerospace Science and Technology，2023，132：108031.

闫斌斌，李勇，戴沛，等.基于增强学习的变体飞行器自适应变体策略与飞行控制方法研究[J].西北工业大学学报，2019，37（4）：656-663.

YAN Binbin，LI Yong，DAI Pei，et al.Adaptive wing morphing strategy and flight control method of a morphing aircraft based on reinforcement learning[J]. Journal of Northwestern Polytechnical University，2019，37（4）：656-663.

XU Wenfeng，LI Yinghui，PEI Binbin，et al.Coordinated intelligent control of the flight control system and shape change of variable sweep morphing aircraft based on dueling-DQN [J].Aerospace Science and Technology，2022，130：107898.

LAMPTON A，NIKSCH A，VALASEK J.Reinforcement learning of morphing airfoils with aerodynamic and structural effects [J].Journal of Aerospace Computing，Information，and Communication，2009，6（1）：30-50.

LAMPTON A，NIKSCH A，VALASEK J.Reinforcement learning of a morphing airfoil-policy and discrete learning analysis [C]//AIAA Guidance，Navigation and Control Conference and Exhibit.Reston，VA，USA：AIAA，2008 ：AIAA2008-7281.

TANDALE M，RONG Jie，VALASEK J.Preliminary results of adaptive reinforcement learning control for morphing aircraft [C]//AIAA Guidance，Navigation，and Control Conference and Exhibit. Reston，VA，USA：AIAA，2004：AIAA 2004-5358.

VALASEK J，TANDALE M D，RONG Jie.A reinforcement learning-adaptive control architecture for morphing [J].Journal of Aerospace Computing，Information，and Communication，2005，2（4）：174-195.

XU Dan，HUI Zhe，LIU Yongqi，et al.Morphing control of a new bionic morphing UAV with deep reinforcement learning [J].Aerospace Science and Technology，2019，92：232-243.

GOECKS V G，LEAL P B C，WHITE T D，et al. Control of morphing wing shapes with deep reinforcement learning [C]//2018 AIAA Information Systems-AIAA Infotech @ Aerospace.Reston，VA，USA：AIAA，2018 ：AIAA2018-2139.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Intelligent Deformation Decision-making Method for Morphing Glide Vehicle with Deep Reinforcement Learning

A Parallel Service Function Chain Deployment Method Combining Federated Learning and Deep Reinforcement Learning

A Precise Control Method for Brain-Computer Cooperation with Deep Reinforcement Learning

Fuzzy Adaptive Deep Reinforcement Learning Method for Optimizing Transient Performance of Servo System

Deep Reinforcement Learning Control Strategy for Primary Frequency Regulation ofEnergy Storage Assisted Thermal Power Units

Related Author

CHEN Gang

MENG Fanyi

WANG Taojun

PANG Ziyuan

DU Jie

LIU Guangyuan

张小栋 1

蒋永玉 1

Related Institution

School of Aerospace Engineering, Xi’an Jiaotong University

School of Information Science and Technology, Shijiazhuang Tiedao University

Hebei Provincial Key Laboratory of Electromagnetic Environmental Effects and Information Processing

Shijiazhuang Key Laboratory of Artificial Intelligence

School of Mechanical Engineering, Xi’an Jiaotong University

⁰