系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (9): 2936-2946.doi: 10.12305/j.issn.1001-506X.2022.09.29

• 通信与网络 • 上一篇    下一篇

固定翼无人机编队构型与通信拓扑优化

徐星光1,2,3,*, 王晓峰1, 姚璐1, 任章2   

  1. 1. 北京机电工程研究所, 北京 100074
    2. 北京航空航天大学自动化科学与电气工程学院, 北京 100191
    3. 复杂系统控制与智能协同技术重点实验室, 北京 100074
  • 收稿日期:2021-05-27 出版日期:2022-09-01 发布日期:2022-09-09
  • 通讯作者: 徐星光
  • 作者简介:徐星光(1988—), 男, 高级工程师, 博士研究生, 主要研究方向为多智能体系统编队协同控制、飞行器总体设计、故障诊断|王晓峰(1983—), 男, 研究员, 博士, 主要研究方向为飞行器总体一体化设计技术、作战仿真技术|姚璐(1996—), 女, 助理工程师, 硕士, 主要研究方向为飞行器总体设计|任章(1957—), 男, 教授, 博士研究生导师, 博士, 主要研究方向为飞行器导航制导与控制、故障诊断、协同控制
  • 基金资助:
    国家自然科学基金(61503009);国家自然科学基金(61971099)

Formation configuration and communication topology optimization for fixed-wing UAVs

Xingguang XU1,2,3,*, Xiaofeng WANG1, Lu YAO1, Zhang REN2   

  1. 1. Beijing Institute of Mechanical and Electrical Engineering, Beijing 100074, China
    2. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
    3. Science and Technology on Complex System Control and Ietelligent Agent Coopration Laboratory, Beijing 100074, China
  • Received:2021-05-27 Online:2022-09-01 Published:2022-09-09
  • Contact: Xingguang XU

摘要:

本文研究了考虑攻防对抗态势与最小信息流要求的固定翼无人机(unmanned aerial vehicles, UAVs)编队构型与通信拓扑优化问题。建立了编队构型指标体系, 给出了大规模集群分层编队构型设计模型和编解码方法, 提出了基于态势场的队形模型, 采用粒子群算法开展了队形参数优化。建立了通信网络拓扑效能指标体系, 提出了通信代价模型, 给出了基于Q学习的网络连通性控制算法。仿真算例验证了所提方法的有效性。

关键词: 固定翼无人机, 编队构型, 通信拓扑

Abstract:

The formation configuration and communication topology optimization problem for fixed-wing unmanned aerial vehicles (UAVs) considering attack defense confrontation situation and minimum information flow requirement is studied. The formation configuration index system is established, the configuration design model and encoding and decoding method of large-scale cluster hierarchical formation are given, the formation model based on power field is proposed, and the formation parameters are optimized by particle swarm optimization algorithm. The communication network topology efficiency index system is established, the communication cost model is proposed, and the network connectivity control algorithm based on Q-Learning is given. The simulation results verify the effectiveness of the proposed method.

Key words: fixed-wing unmanned aerial vehicles (UAVs), formation configuration, communication topology

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