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基于改进粒子群算法的飞行器协同轨迹规划

周宏宇 王小刚 单永志 赵亚丽 崔乃刚

周宏宇, 王小刚, 单永志, 赵亚丽, 崔乃刚. 基于改进粒子群算法的飞行器协同轨迹规划. 自动化学报, 2022, 48(11): 2670−2676 doi: 10.16383/j.aas.c190865
引用本文: 周宏宇, 王小刚, 单永志, 赵亚丽, 崔乃刚. 基于改进粒子群算法的飞行器协同轨迹规划. 自动化学报, 2022, 48(11): 2670−2676 doi: 10.16383/j.aas.c190865
Zhou Hong-Yu, Wang Xiao-Gang, Shan Yong-Zhi, Zhao Ya-Li, Cui Nai-Gang. Synergistic path planning for multiple vehicles based on an improved particle swarm optimization method. Acta Automatica Sinica, 2022, 48(11): 2670−2676 doi: 10.16383/j.aas.c190865
Citation: Zhou Hong-Yu, Wang Xiao-Gang, Shan Yong-Zhi, Zhao Ya-Li, Cui Nai-Gang. Synergistic path planning for multiple vehicles based on an improved particle swarm optimization method. Acta Automatica Sinica, 2022, 48(11): 2670−2676 doi: 10.16383/j.aas.c190865

基于改进粒子群算法的飞行器协同轨迹规划

doi: 10.16383/j.aas.c190865
基金项目: 中国博士后科学基金(2019M661290)资助
详细信息
    作者简介:

    周宏宇:哈尔滨工业大学讲师. 主要研究方向为飞行器轨迹优化.E-mail: sd4574462@foxmail.com

    王小刚:哈尔滨工业大学副教授. 主要研究方向为飞行器制导, 导航与控制. 本文通信作者.E-mail: wangxiaogang@hit.edu.com

    单永志:哈尔滨国营六二四厂研究员. 主要研究方向为飞行器总体设计.E-mail: yongzhi0451@sina.com

    赵亚丽:北京航天晨信科技有限公司研究员. 主要研究方向为优化设计与系统工程.E-mail: zhaoyali_aerocim@163.com

    崔乃刚:哈尔滨工业大学教授. 主要研究方向为飞行器制导, 导航与控制.E-mail: cui_naigang@163.com

Synergistic Path Planning for Multiple Vehicles Based on an Improved Particle Swarm Optimization Method

Funds: Supported by National Postdoctoral Science Foundation of China (2019M661290)
More Information
    Author Bio:

    ZHOU Hong-Yu Lecturer at Harbin Institute of Technology. His research interest covers trajectory op-timization of vehicles

    WANG Xiao-Gang Associate professor at Harbin Institute of Technology. His research interest covers guidance, navigation and guidance of vehicles. Corresponding author of this paper

    SHAN Yong-Zhi Professor at State-owned Factory No. 624. His main research interest is overall design of vehicles

    ZHAO Ya-Li Professor at Beijing Aerocim Technology Co., Ltd.. Her research interest covers optimization design and system engineering

    CUI Nai-Gang Professor at Harbin Institute of Technology. His research interest covers guidance, naviga-tion and guidance of vehicles

  • 摘要: 考虑气动、轨迹、约束、指标间的耦合关系, 以多高超声速飞行器同时到达为目标建立了协同规划模型; 设计了一种自动满足终端约束的全新滑翔飞行剖面, 减少了规划算法需要处理的约束数量; 推导了滑翔段高精度解析解, 实现了过程约束和性能指标的快速求解; 提出了一种改进粒子群优化(Particle swarm optimization, PSO)算法, 借助强化学习方法构建协同需求与惯性权重间的动态映射网络, 提高了在线规划效率. 最后通过数学仿真验证了方法的正确性和有效性.
  • 图  1  在线规划算法流程图

    Fig.  1  The flowchart of the planning algorithm

    图  2  滑翔段飞行剖面

    Fig.  2  Flight profiles of different vehicles

    图  3  飞行路径角随时间变化情况

    Fig.  3  Time histories of the flight path angle

    图  4  过程约束随时间变化情况

    Fig.  4  Time histories of the path constraints

    图  5  攻角和倾侧角随时间变化情况

    Fig.  5  Histories of angle-of-attack and the bank angle

    图  6  攻角随时间变化情况(飞行器1)

    Fig.  6  Histories of the angle-of-attack (Vehicle 1)

    图  7  动压随时间变化情况(飞行器1)

    Fig.  7  Histories of the dynamic pressure (Vehicle 1)

    表  1  初始状态和终端约束

    Table  1  The initial states and the terminal constraints

    初始值经纬度 (°)高度 (km)速度 (m/s)飞行路径角 (°)剩余航程 (km)
    飞行器 1E 90, N 45565400−2.02304
    飞行器 2E 65, N 30545300−2.02263
    飞行器 3E 40, N 35525200−1.02324
    飞行器 4E 70, N 70505100−1.02285
    终端值E 60, N 5025−1.00
    下载: 导出CSV

    表  2  基本PSO和改进PSO计算效率对比

    Table  2  Comparison of the computation efficiency

    进化代数最大值最小值平均值标准差
    基本 PSO208143.17
    改进 PSO166102.69
    下载: 导出CSV

    表  3  滑翔段干扰因素设置

    Table  3  Disturbances in the glide phase

    序号干扰因素$3\sigma $值
    1初始速度 (m/s)30
    2初始飞行路径角 (°)0.2
    3初始高度 (m)500
    4初始航向偏差 (°)0.2
    5初始剩余航程 (km)50
    6气动系数 (%)10
    7大气密度 (%)10
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-12-20
  • 网络出版日期:  2022-10-25
  • 刊出日期:  2022-11-22

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