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基于RQPSO-DMPC的多无人机编队自主重构控制方法

周绍磊 康宇航 史贤俊 戴邵武 周超

周绍磊, 康宇航, 史贤俊, 等 . 基于RQPSO-DMPC的多无人机编队自主重构控制方法[J]. 北京航空航天大学学报, 2017, 43(10): 1960-1971. doi: 10.13700/j.bh.1001-5965.2016.0777
引用本文: 周绍磊, 康宇航, 史贤俊, 等 . 基于RQPSO-DMPC的多无人机编队自主重构控制方法[J]. 北京航空航天大学学报, 2017, 43(10): 1960-1971. doi: 10.13700/j.bh.1001-5965.2016.0777
ZHOU Shaolei, KANG Yuhang, SHI Xianjun, et al. Autonomous reconfiguration control method for multi-UAV formation based on RQPSO-DMPC[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(10): 1960-1971. doi: 10.13700/j.bh.1001-5965.2016.0777(in Chinese)
Citation: ZHOU Shaolei, KANG Yuhang, SHI Xianjun, et al. Autonomous reconfiguration control method for multi-UAV formation based on RQPSO-DMPC[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(10): 1960-1971. doi: 10.13700/j.bh.1001-5965.2016.0777(in Chinese)

基于RQPSO-DMPC的多无人机编队自主重构控制方法

doi: 10.13700/j.bh.1001-5965.2016.0777
基金项目: 

国家自然科学基金 61102167

航空科学基金 20135184007

详细信息
    作者简介:

    周绍磊, 男, 博士, 教授。主要研究方向:多无人机编队控制、故障诊断; 康宇航, 男, 博士研究生。主要研究方向:多无人机编队控制

    康宇航 男,博士研究生。主要研究方向:多无人机编队控制。

    通讯作者:

    周绍磊, E-mail: 492292699@qq.com

  • 中图分类号: V249.122;TP391

Autonomous reconfiguration control method for multi-UAV formation based on RQPSO-DMPC

Funds: 

National Natural Science Foundation of China 61102167

Aeronautical Science Foundation of China 20135184007

More Information
  • 摘要:

    针对敌方防御区域内各种威胁,为了实现隐蔽突防并实施对敌有效打击,在突防过程中多无人机(UAV)编队需要进行重构控制,并且编队内的相互避碰问题与通信约束问题也需考虑。通过建立无人机虚拟领航编队模型并引入邻居集,采用分布式模型预测控制(DMPC)同时构建多无人机编队的重构代价函数,提出采用改进量子粒子群优化(RQPSO)算法进行求解,并将求解结果与采用粒子群优化算法的结果进行对比。仿真结果表明,本文算法能够有效控制多无人机编队完成自主重构,实现安全隐蔽突防任务。

     

  • 图 1  模型预测控制原理图

    Figure 1.  Schematic of model predictive control

    图 2  虚拟领航编队控制模型

    Figure 2.  Virtual leader formation control model

    图 3  预警雷达与干扰示意图

    Figure 3.  Schematic of early warning radar and interference

    图 4  防空雷达与干扰示意图

    Figure 4.  Schematic of air-defense radar and interference

    图 5  禁飞区或障碍威胁示意图

    Figure 5.  Schematic of non-fly zone or obstacle threat

    图 6  无人机uvi的紧约束邻居示意图

    Figure 6.  Schematic of close constraint neighbor of UAV uvi

    图 7  分布式协同编队构型重构原理框图

    Figure 7.  Principle block diagram of distributed cooperating formation reconfiguration

    图 8  3种优化航迹示意图

    Figure 8.  Schematic of three types of optimized trajectory

    图 9  粒子编码

    Figure 9.  Particle coding

    图 10  威胁环境下无人机编队自动构型变换轨迹

    Figure 10.  Automatic configuration transforming flight trajectory of UAV formation in threatened environment

    图 11  无人机uv1速度指令及响应曲线

    Figure 11.  Velocity instruction and response curves of uv1

    图 12  无人机uv1航向角指令及响应曲线

    Figure 12.  Course angle instruction and response curves of uv1

    图 13  无人机期望位置跟踪偏差曲线

    Figure 13.  Desired location tracking deviation curves of UAVs

    图 14  无人机之间相对距离

    Figure 14.  Relative distance between UAVs

    图 15  RQPSO算法与PSO算法收敛速度比较

    Figure 15.  Comparison of convergence speed between RQPSO and PSO algorithms

    表  1  无人机初始运动参数

    Table  1.   Initial kinematic parameters of UAVs

    无人机 (x, y)/km v/(m·s-1) χ/(°)
    uv1 (3.8, 8.2) 152 90
    uv2 (2.8, 7.7) 152 90
    uv3 (2.8, 8.7) 152 90
    uv4 (1.8, 7.2) 152 90
    uv5 (1.8, 9.2) 152 90
    下载: 导出CSV

    表  2  参考轨迹参数

    Table  2.   Parameters of reference trajectory

    参数 时间/s 数值
    初始位置 0 (2.8, 8.2) km
    速度 [0,200] 152m/s
    航向 [0,200] 90°
    下载: 导出CSV

    表  3  编队初始构型参数

    Table  3.   Parameters of Initial formation configuration

    初始坐标 数值
    (x1rd,x1rd ) (0,1/sin(π/3))
    (x2rd,x2rd ) (0.5,0.5/sin(π/6))
    (x3rd,x3rd ) (-0.5,0.5/sin(π/6))
    (x4rd,x4rd ) (1,-0.5/cos (π/6))
    (x5rd,x5rd ) (-1,-0.5/cos (π/6))
    下载: 导出CSV

    表  4  敌方威胁参数

    Table  4.   Parameters of enemy threat

    参数 数值
    预警雷达坐标/km (30, 20)
    预警雷达作用距离/km 20
    防空雷达坐标/km (32, 18)
    防空雷达作用距离/km 12
    防空雷达角度/(°) 10
    禁飞区1区间范围/km [(12, 12), (18, 12), (18, 18), (12, 5)]
    禁飞区2区间范围/km [(25, 25), (35, 25), (35, 5), (25, 5)]
    机间防碰撞距离/km 0.5
    最大通信距离/km 2
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-10-07
  • 录用日期:  2016-11-04
  • 网络出版日期:  2017-10-20

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