可变构型飞行器自主变形决策技术研究

凡文帅; 许红羊; 赵振坤; 凡永华

doi:10.12382/bgxb.2025.0900

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可变构型飞行器自主变形决策技术研究

更新时间：2026-02-10

- 可变构型飞行器自主变形决策技术研究
- Research on Autonomous Morphing Decision-Making Technology
- 兵工学报 2026年
- 作者机构：
  
  1. 西北工业大学航天学院,陕西,西安,710129
  2. 中国空空导弹研究院,河南,洛阳,471009
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0900
  中图分类号： V249
- 收稿：2025-09-30，
  
  网络首发：2026-02-10，
- 稿件说明：
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凡文帅，许红羊，赵振坤，等. 可变构型飞行器自主变形决策技术研究[J/OL]. 兵工学报, 2026(2026-02-10). https://doi.org/10.12382/bgxb.2025.0900.

FAN W N, XU H N, ZHAO Z N, et al. Research on autonomous morphing decision-making technology[J/OL]. Acta Armamentarii, 2026(2026-02-10). https://doi.org/10.12382/bgxb.2025.0900. (in Chinese)
凡文帅，许红羊，赵振坤，等. 可变构型飞行器自主变形决策技术研究[J/OL]. 兵工学报, 2026(2026-02-10). https://doi.org/10.12382/bgxb.2025.0900. DOI：

FAN W N, XU H N, ZHAO Z N, et al. Research on autonomous morphing decision-making technology[J/OL]. Acta Armamentarii, 2026(2026-02-10). https://doi.org/10.12382/bgxb.2025.0900. (in Chinese) DOI：

摘要

针对一种可变化展长、后掠角的飞行器通过自主变形决策来获取最小阻力外形的问题，提出基于专家引导记忆回放的双延迟深度确定性策略梯度（Twin Delayed Deep Deterministic policy gradient，TD3的自主变形技术。根据飞行器典型任务需求构建了变体飞行器运动模型以及由变体引起的非线性气动变化特征。对TD3算法进行改进，设计了E-M-TD3算法的训练步骤，通过判断飞行器在执行大空域下多样化任务时的动压变化趋势以及攻角变化趋势，结合期望外形设计了变形过程中的专家引导机制；通过将记忆缓冲区接入TD3网络，将得到当前策略的评价值与动作策略网络输出该策略评价值做对比，来更快地选择最优策略。提出的E-M-TD3算法框架实现了飞行器在典型任务场景中全过程阻力最小并兼具机动性能。仿真结果表明：新算法快速收敛，训练完成得到的飞行器构型在飞行过程中所受阻力可下降7%，在能量消耗方面具有显著优势。

Abstract

In order toachievethe minimum-drag configuration for an aircraft with variable wingspan and sweep angle through autonomous morphing decision-making

this paper proposes an autonomous morphing technologybased on the TD3 (twindelayeddeepdeterministicpolicygradient) algorithm enhanced by expert-guided memory replay

termedexpert-memory-TD3(E-M-TD3).Amotion model for the morphing aircraft and the characteristics of nonlinear aerodynamic variations induced by morphing are established according to the typical mission requirements. Subsequently

the TD3 algorithmis improved

and the training procedure of the E-M-TD3 algorithm is designed. By analyzing thevariationtrends of dynamic pressure and angle of attackwhen the aircraft is performingdiverse missionsin alarge airspace

an expert-guided mechanism is incorporated into the morphing processin conjunction with the desired configuration.Anoptimal strategy can be selected more rapidlyby integrating a memory replay buffer into the TD3 network and comparing the evaluatedvalueof the current policy with theestimatedvalueofthe policy networkoutputtedby the critic network for theactions. The proposed E-M-TD3 framework ensures minimal drag throughout the entire mission profile in typical operational scenarios while maintaining maneuverability. Simulatedresults demonstrate that theproposedalgorithm converges quickly. The trained aircraft configuration achieves a 7% reduction in drag during flight

exhibitingasignificant advantageinterms ofenergy consumption.

关键词

Keywords

references

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