吉林大学学报(工学版) ›› 2023, Vol. 53 ›› Issue (3): 871-882.doi: 10.13229/j.cnki.jdxbgxb20221034

• 通信与控制工程 • 上一篇    

基于状态观测器的多四旋翼无人机时变编队控制

于雅静1(),郭健1,王荣浩2,秦伟3,4,宋明武4,向峥嵘1()   

  1. 1.南京理工大学 自动化学院，南京 210094
    2.陆军工程大学 国防工程学院，南京 210007
    3.军事科学院 国防科技创新研究院，北京 100091
    4.天津（滨海）人工智能创新中心，天津 300450
  • 收稿日期:2022-08-15 出版日期:2023-03-01 发布日期:2023-03-29
  • 通讯作者: 向峥嵘 E-mail:yajing.yu@njust.edu.cn;xiangzr@njust.edu.cn
  • 作者简介:于雅静（1990-），女，博士研究生. 研究方向：多智能体、无人机编队控制. E-mail：yajing.yu@njust.edu.cn
  • 基金资助:
    国家自然科学基金项目(61873128);江苏省重点研发计划重点项目(BE2021016);江苏省“六大人才高峰”高层次人才项目(GDZB-027)

Time⁃varying formation control of multi⁃quadrotor unmanned aerial vehicles based on state observer

Ya-jing YU1(),Jian GUO1,Rong-hao WANG2,Wei QIN3,4,Ming-wu SONG4,Zheng-rong XIANG1()   

  1. 1.School of Automation，Nanjing University of Science and Technology，Nanjing 210094，China
    2.Academy of National Defense Engineering，Army Engineering University，Nanjing 210007，China
    3.Defense Innovation Institute，Academy of Military Sciences，Beijing 100091，China
    4.Tianjin Artificial Intelligence Innovation Center，Tianjin 300450，China
  • Received:2022-08-15 Online:2023-03-01 Published:2023-03-29
  • Contact: Zheng-rong XIANG E-mail:yajing.yu@njust.edu.cn;xiangzr@njust.edu.cn

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摘要：

针对有向切换通信网络下多四旋翼无人机系统存在的部分信息不可测问题，基于神经网络状态观测器方法，提出了分布式自适应滑模时变编队控制方案。首先，通过设计分布式时变编队控制协议，使无人机仅需邻居状态信息便可实现通信，摆脱了对全局信息的依赖。其次，由于欠驱动特性的存在，设计了位置辅助控制器，解算出姿态目标信息和控制推力。同时，通过设计神经网络状态观测器，对系统不可测信息进行观测，并将观测值反馈到自适应滑模控制器中，提高了无人机系统的鲁棒性。最后，通过Lyapunov定理对四旋翼无人机编队系统进行分析，证明了多无人机编队追踪误差有界且可收敛到零点邻域内。仿真结果表明，本文控制方法可以使多无人机系统实现时变编队控制，验证了理论结果的有效性。

关键词: 无人机系统, 有向切换通信网络, 神经网络状态观测器, 时变编队控制, 自适应滑模控制

Abstract:

A distributed adaptive sliding mode time-varying formation control scheme based on neural network state observer was proposed to solve the problem of unmeasurable information of multi-quadrotor unmanned aerial vehicles （UAVs） system in directed switching communication network. Firstly， by designing a distributed time-varying formation control protocol， UAVS can communicate with each other only by the status information of their neighbors， which is free from the dependence on global information. Due to the underdrive characteristic， a position-assisted controller was designed to solve the target information of two attitude angles and control thrust. At the same time， a neural network state observer was designed to observe the unmeasurable information of the system， and the observed values were feedback to the adaptive sliding mode controller in real time， which improves the robustness of the UAV system. The quadrotor UAV formation system is analyzed by Lyapunov's theorem， and it proved that the multi-UAV formation error is bounded and converges to near zero. The simulation results show that the proposed control method can realize the time-varying formation control of the multi-UAV system and verify the validity of the theoretical results.

Key words: unmanned aerial vehicles system, directed switching communications network, neural network state observer, time-varying formation control, adaptive sliding mode control

中图分类号: 

  • V249.121

图1

四旋翼无人机结构图"

图2

双环控制系统结构图"

图3

可能的通信网络结构"

表1

无人机初始状态"

名称位置/m速度/（m·s-1）姿态角/（°）
UAV1（-50，50，0）（1.5，1.5，1.5）（0，0，0）
UAV2（-55，75，0）（1.2，1.2，1.2）（0，0，0）
UAV3（75，30，0）（1.6，1.6，1.6）（0，0，0）
UAV4（150，10，0）（1.2，1.2，1.2）（0，0，0）

图4

无人机时变编队3D轨迹图"

图5

控制输入"

图6

自适应律"

图7

四架无人机位置、速度和姿态曲线及其观测误差"

图8

四架无人机追踪误差"

图9

不同算法无人机2位置、速度和姿态观测误差曲线"

图10

两种不同算法无人机2追踪误差曲线"

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