Target Enclosing Control of Unmanned Aerial Vehicle Swarm Based on Socialized Collaboration
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摘要: 面向感知、通信受限且存在环境障碍的移动目标合围控制, 提出一种基于社会化协同的控制策略. 首先, 借鉴生物集群社会化行为, 构建协同响应模型与层级交互机制; 在拓扑切换与丢包条件下, 显式建模受限信息流, 以驱动集群实现目标合围. 其次, 提出强引导式任务-避碰并行协同控制, 在优先保障飞行安全的前提下实现鲁棒合围控制. 再次, 设计一致性目标状态观测器, 对目标位置与速度进行稳健估计. 最后, 仿真结果表明, 所提方法在障碍环境以及感知、通信受限条件下能够实现稳定合围, 并表现出较好的鲁棒性.Abstract: A socialized-collaboration-based control strategy is proposed for mobile-target encirclement under perception and communication constraints in the presence of environmental obstacles. First, inspired by socialized behaviors in biological swarms, a cooperative response model and a hierarchical interaction mechanism are established; under topology switching and packet-loss conditions, the constrained information flow is explicitly modeled to drive the swarm to achieve target encirclement. Second, a strongly guided task–collision-avoidance parallel cooperative control scheme is proposed to realize robust encirclement while prioritizing flight safety. Third, a consensus-based target-state observer is designed to robustly estimate the target position and velocity. Finally, simulation results demonstrate that the proposed method can achieve stable encirclement in obstacle-laden environments under perception and communication constraints, exhibiting favorable robustness.
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图 2 目标观测器性能分析图
(a) 目标状态与估计对比 (b) 平均绝对误差与均方根误差
Fig. 2 Performance analysis of the target-state observer
(a) comparison between true and estimated target states (b) temporal evolution of mean absolute error and root mean square error
图 3 无人机集群目标合围飞行三维轨迹
Fig. 3 3D trajectories of the UAV swarm during target enclosing task
图 8 不同失效比例下的避碰成功率
Fig. 8 Collision-avoidance success rate under different failure ratios
表 1 仿真参数设置
Table 1 Settings of simulation parameters
类别 符号 数值 无人机集群规模 $ N $ $ 7 $ 速度上下界 $ ({V}_{\min },\;{V}_{\max }) $ $ (10,\;80)\;\mathrm{m}/\mathrm{s} $ 最大过载 $ {n}_{\max } $ $ 6 $ 最大航迹角 $ {\gamma }_{\max } $ $ \pi /4 $ 自动驾驶仪时间常数 $ {\tau }_{v},\;{\tau }_{\chi },\;{\tau }_{\gamma } $ $ 2.5\;\mathrm{s} $,$ 2.5\;\mathrm{s},\;2.5\;\mathrm{s} $ 高度控制增益常数 $ {k}_{1},\;{k}_{2} $ $ 2,\;2 $ 感知半径 $ {r}_{d} $ $ 200\;\mathrm{m} $ 通信距离 $ {r}_{c} $ $ 200\;\mathrm{m} $ 期望合围半径 $ {r}^{\mathrm{*}} $ $ 100\;\mathrm{m} $ 期望角间距 $ \varphi _{iT}^{\mathrm{*}} $ $ -\pi $ 观测器收敛系数 $ \alpha $ $ 2 $ 排斥力系数 $ {\rho }_{a} $ $ 2 $ 安全距离 $ {d}_{s} $ $ 150\;\mathrm{m} $ 控制增益 $ {\gamma }_{1},\;{\gamma }_{2},\;{\gamma }_{3},\;{\gamma }_{4} $ $ 5,\;1,\;5,\;8 $ 社会力权重系数 $ {w}_{co},\;{w}_{T},\; $
$ {w}_{d},\;{w}_{w} $$ 0.4,\;0.4,\; $
$ 0.15,\;0.05 $
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表 2 障碍物参数设置
Table 2 Settings of obstacle parameters
障碍物标号 中心点坐标 范围半径 1 (400, 280) 150 m 2 (800, −280) 50 m 3 ( 1200 , 150)150 m 4 ( 1600 , −150)50 m 5 ( 2300 , 150)150 m
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表 3 三种算法对比仿真统计结果
Table 3 Simulation statistics results for three algorithms comparison
指标 VFM RFM 本文 合围时间$ {T}_{c}(\mathrm{s}) $ $ 82.4\pm 6.8 $ $ 95.7\pm 9.3 $ $ \mathbf{59}.\mathbf{3}\pm \mathbf{5}.\mathbf{4} $ 均方误差$ {E}_{r}\left(\mathrm{m}\right) $ $ 5.1\pm 1.7 $ $ 6.8\pm 2.1 $ $ \mathbf{3}.\mathbf{2}\pm \mathbf{1}.\mathbf{0} $ 避碰成功率$ {P}_{c}(\% ) $ $ 71.2\pm 4.9 $ $ 65.5\pm 6.3 $ $ \mathbf{93}.\mathbf{8}\pm \mathbf{3}.\mathbf{1} $ RMSE $ 3.5\pm 0.8 $ $ 4.1\pm 1.0 $ $ \mathbf{2}.\mathbf{4}\pm \mathbf{0}.\mathbf{7} $
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