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DOI: https://doi.org/10.7527/S1000-6893.2026.33379

机动轨迹强化粒子群优化与总能量抗扰控制-自主智能无人系统专刊

  • 董哲 ,
  • 刘凯 ,
  • 王冠 ,
  • 王振威
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  • 1. 大连理工大学
    2. 大连理工大学力学与航空航天学院

收稿日期: 2026-01-15

  修回日期: 2026-04-28

  网络出版日期: 2026-04-30

基金资助

国家自然科学基金;国防基础科研计划项目;装备预研教育部联合基金;中央高校基本科研业务

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Reinforcement-enhanced particle swarm optimization of maneuver trajectories and total-energy anti-disturbance control

  • DONG Zhe ,
  • LIU Kai ,
  • WANG Guan ,
  • WANG Zhen-Wei
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Received date: 2026-01-15

  Revised date: 2026-04-28

  Online published: 2026-04-30

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摘要

针对无人作战飞机高动态近距空战场景下的机动动作优化与轨迹跟踪控制问题，综合考虑空战态势指标和机动能量设计了面向筋斗、殷麦曼、高YO-YO和滚筒四类典型战术机动的轻量强化学习粒子群轨迹优化算法与总能量轨迹线性化跟踪控制框架。首先，建立了战术机动中耦合迎角影响的推力模型与融合机动能量信息的过载边界模型，支撑战术机动优化与考虑飞推耦合影响的总能量线性化控制设计；其次，以迎角、油门与速度滚转角的变化率作为指令单元，构建了四类机动的指令单元分段模型，进而设计了融合Q-Learning粒子学习范式决策机制与K-最近邻域差分进化的改进粒子群算法，开展基于相对位置、角度、过载与推力做功等指标的四类战术机动轨迹优化；然后，将优化得到的指令单元转换为总能量变化率与分配率指令，通过总能量增广动力学方程线性化与期望系统时域响应增益配置设计了面向优化机动轨迹的总能量线性化跟踪控制律，同时引入基于在线气动辨识的自适应修正系数以补偿气动摄动下的控制精度；最后，面向四类战术机动开展了针对智能改进粒子群机动优化算法和总能量线性化轨迹控制律的数值仿真验证，结果表明所设计的方法能够有效降低机动能量损耗与优化耗时，同时显著提升飞推耦合与气动摄动情况下的控制精度与鲁棒性。

关键词： 战术机动优化; 强化学习; 进化粒子群优化; 机动能量; 总能量控制; 迎角/推力耦合

本文引用格式

董哲 , 刘凯 , 王冠 , 王振威 . 机动轨迹强化粒子群优化与总能量抗扰控制-自主智能无人系统专刊[J]. 航空学报, 0 : 1 -0 . DOI: 10.7527/S1000-6893.2026.33379

Abstract

For high dynamic within visual range air combat of unmanned combat aerial vehicle (UCAV), this paper addresses tactical-maneuver trajectory optimization and trajectory tracking control. By jointly considering air combat situational metrics and maneuver energy, a lightweight reinforcement learning particle swarm trajectory optimizer and a total energy linearized tracking control framework are developed for four representative tactical maneuvers: Loop, Immelmann, High Yo-Yo, and Barrel Roll. First, an angle-of-attack (AOA) coupled thrust model and a load factor envelope model incorporating maneuvering energy information are established to support maneuver optimization and total energy control design under propulsion–airframe coupling. Then, using the rates of change of AOA, throttle, and velocity roll angle as command primitives, the segmented command models are constructed for the four maneuvers. An improved particle swarm optimization algorithm is then proposed by integrating a Q-learning-driven particle learning paradigm and a K-nearest neighbor differential-evolution mechanism, enabling maneuver trajectory optimization with respect to relative geometry, attitude, load factor, and thrust work-related metrics. Thereafter, the optimized trajectory commands are converted into total energy rate and energy-allocation rate commands. A total energy linearized tracking law is derived via linearization of the augmented total energy dynamics and desired time-domain response-based gain assignment. An adaptive correction factor based on online aerodynamic identification is further introduced to compensate for aerodynamic perturbations. Finally, numerical simulations on the four tactical maneuvers validate the proposed intelligent maneuver optimizer and total energy linearized controller. Results demonstrate reduced maneuver energy loss and optimization time, and significantly improved tracking accuracy and robustness under propulsion–airframe coupling and aerodynamic perturbations.

Key words： tactical maneuvers optimization; reinforcement learning; evolutionary particle swarm optimization; maneuvering energy; total energy control; AOA/thrust coupling

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