典型地域风场下分布式 UAV 编队自适应时延控制
DOI:
CSTR:
作者:
作者单位:

1.南京信息工程大学 自动化学院,南京 210044; 2.南京信息工程大学 人工智能学院,南京 210044; 3.临界空间环境特性及效应全国重点实验室,南京 210044; 4.大气环境与装备技术协同创新中心,南京 210044; 5.大数据分析与智能系统江苏省高校重点实验室,南京 210044

作者简介:

通讯作者:

中图分类号:

基金项目:


Adaptive Time-Delay Control of Distributed UAV Formation under Typical Mountainous Wind Field
Author:
Affiliation:

1.College of Automation, Nanjing University of Information Science and Technology, Nanjing 210044; 2.College of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044; 3.State Key Laboratory of Environment Characteristics and Effects for Near-space, Nanjing University of Information Science and Technology, Nanjing 210044; 4.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology(CICAEET), Nanjing 210044; 5.Jiangsu Provincial University Key laboratory of Big Data Analysis and Intelligent Systems, Nanjing 210044

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    针对典型山地风场条件下多无人机(Unmanned Aerial Vehicle,UAV)编队通信时延问题,提出了一种时序相关性时变时延分布式自适应控制算法,以增强编队在复杂风场环境下的控制性能和抗干扰能力。首先,基于多重复合风构建了典型山地风场模型,并在此基础上建立了多无人机编队模型。其次,提出了一种基于时序相关性的自适应时延控制算法,利用偏自相关系数(the Partial Autocorrelation Function, PACF)来实现对时延和本身状态的时序调控。然后,在控制协议中引入编队控制补偿向量,通过反馈增益矩阵将目标状态输入至控制协议中,更精准地跟踪目标状态;该算法还通过Lur’e非线性拟合非线性特性,处理了风场和无人机自身非线性特性所带来的双重耦合影响,移除了对非线性有界处理的依赖;算法中应用自适应律来估计并控制风场环境的噪声干扰;算法还采用非光滑函数来模拟未知环境干扰,同时结合自适应增益对系统动态特性进行补偿。最后,通过数值仿真验证了所提出的控制协议的有效性,说明了该算法能够提高无人机编队在山地风场中的稳定性和适应性。

    Abstract:

    Aiming at the problem of communication delay in multi-unmanned aerial vehicles (UAVs) under a typical mountainous wind field, a time-varying delay distributed adaptive control algorithm based on temporal correlation is proposed, which has been designed to improve its time-varying delay control capability and enhance its anti-disturbance ability under this wind field. Firstly, a typical mountainous wind field model is constructed based on multi-compound winds, Building on this, the formation model of multiple UAVs is established. Secondly, an adaptive delay control algorithm based on temporal correlation is proposed, utilizing the Partial Autocorrelation Function (PACF) to achieve temporal regulation of both the delays and the state itself. In the control protocol, a formation control compensation vector is introduced, and the target state is input into the control protocol through a feedback gain matrix, allowing for more precise tracking of the target state. This algorithm also uses Lur'e nonlinearity to handle the dual coupling effects caused by the nonlinear characteristics of the wind field and the UAV itself, which removes the dependence on nonlinear bounded treatments. The adaptive law is applied in the algorithm to estimate the noise disturbance under the wind field. A non-smooth function is used to simulate unknown environmental disturbance, and an adaptive gain is designed to compensate for the dynamic characteristics of the system. Finally, the effectiveness of the proposed control protocol is verified through numerical simulations, demonstrating that the algorithm can improve the stability and adaptability of UAV formation under mountainous wind fields.

    参考文献
    相似文献
    引证文献
引用本文

周佶辰,葛泉波,南晓娅,李涛.典型地域风场下分布式 UAV 编队自适应时延控制[J].南京航空航天大学学报,,():

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2025-07-05
  • 出版日期:
文章二维码
您是第位访问者
网站版权 © 南京航空航天大学学报
技术支持:北京勤云科技发展有限公司