Dynamic Event-Triggered Distributed Average Tracking for Cluster Unmanned Aerial Vehicles in Low-Altitude Countermeasures
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摘要: 摘要应涵盖全文. 摘要内容包括研究目的、方法、结果等, 注意不是标题的罗列, 能独立成文, 不能出现公式号和文献号. 英文摘要的书写, 请按英语习惯, 无文法及拼写错误, 用词准确. 无人机技术的普及与滥用带来严峻空防挑战, 民用无人机“黑飞”威胁公共安全与基础设施, 军用无人机及蜂群作战突破传统防空体系, 单一防御手段已难以应对, 提升对非合作无人机的协同跟踪与处置能力迫在眉睫. 飞网协同围捕技术作为无人机反制核心方案之一, 要求多无人机协同估计目标轨迹中心并自主编队完成捕获, 而分布式平均跟踪是实现该目标的关键理论. 本文针对此问题, 提出无需相对速度测量和变量初始化的集群无人机系统动态事件触发分布式平均跟踪算法; 理论分析证实, 该算法可实现分布式平均跟踪目标且闭环系统不存在芝诺现象; 最后以集群无人机跟踪非合作无人机集群为实例, 验证了算法的有效性与实用性.Abstract: The popularization and abuse of unmanned aerial vehicle (UAV) technology have brought severe challenges to air defense. Civil UAVs with unauthorized flights threaten public security and infrastructure, while military UAVs and swarm combat have broken through the traditional air defense system, making a single defense method insufficient to deal with such threats. Therefore, it is extremely urgent to enhance the collaborative tracking and handling capabilities for non-cooperative UAVs. As one of the core solutions for UAV countermeasure, the cooperative encirclement technology based on flying net requires multiple UAVs to collaboratively estimate the trajectory center of the target and form teams autonomously to achieve capture, and distributed average tracking (DAT) is the key theory to realize this goal. Aiming at this problem, this paper proposes a dynamic event-triggered DAT algorithm for cluster UAV systems without relative velocity measurement and variable initialization. Theoretical analysis verifies that the proposed algorithm can achieve the goal of DAT and there is no Zeno behavior in the closed-loop system. Finally, the effectiveness and practicability of the algorithm are verified by an example of cluster UAVs tracking non-cooperative UAV clusters.
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图 1 无人机集群系统目标观测及通信关系示意图
Fig. 1 Schematic diagram of the target observation and communication relationship in the UAV cluster system
图 2 在示例1参数下，每架无人机的运动轨迹和非合作无人机的平均运动轨迹
Fig. 2 Motion trajectories of each UAV and the average motion trajectory of the non-cooperative UAVs under the parameters in Example 1
图 4 在示例1参数下, 动态耦合增益$\pi_{ij}$的状态演化
Fig. 4 State evolution of the dynamic coupling gain $\pi_{ij}$ under the parameter in Example 1
图 5 在示例2参数下，每架无人机的运动轨迹和非合作无人机的平均运动轨迹
Fig. 5 Motion trajectories of each UAV and the average motion trajectory of the non-cooperative UAVs under the parameters in Example 2
图 7 在示例2参数下, 动态耦合增益$\pi_{ij}$的状态演化
Fig. 7 State evolution of the dynamic coupling gain $\pi_{ij}$ under the parameter in Example 2
图 3 在示例1参数下, 无人机位置和速度的跟踪误差
Fig. 3 Tracking errors of the position and velocity of each UAV under the parameters in Example 1
图 6 在示例2参数下, 无人机位置和速度的跟踪误差
Fig. 6 Tracking errors of the position and velocity of each UAV under the parameters in Example 2
表 1 不同参数选择下, 每架无人机的通信次数
Table 1 The number of communications of each UAV under different parameter selections
无人机 示例1 示例2 1 3729 1171 2 2537 581 3 4579 1239 4 5143 741 5 6403 1399 6 3293 983 
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