不确定干扰下四旋翼无人机集群数据驱动滑模抗扰编队控制

黎乾雄; 路晓庆

doi:10.11999/JEIT260050

不确定干扰下四旋翼无人机集群数据驱动滑模抗扰编队控制

doi: 10.11999/JEIT260050 cstr: 32379.14.JEIT260050

黎乾雄,
路晓庆^,

武汉大学电气与自动化学院武汉 430072

基金项目: 国家重点研发计划(2022YFB3903804)

详细信息

作者简介:
黎乾雄：男，博士生，研究方向为无人集群协同控制

路晓庆：女，教授，研究方向为多机器人协作等复杂系统智能控制

通讯作者:
路晓庆　luxq@whu.edu.cn

中图分类号: TP24
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- 被引次数: 0
出版历程
- 收稿日期: 2026-01-14
- 修回日期: 2026-04-17
- 录用日期: 2026-04-23
- 网络出版日期: 2026-05-13

Data-driven Sliding-mode Disturbance-rejection Formation Control for Quadrotor UAV Swarms Under Uncertain Disturbances

LI Qianxiong,
LU Xiaoqing^,

School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Funds: The National Key Research and Development Program of China(2022YFB3903804)

摘要

摘要: 不确定扰动环境下四旋翼无人机(UAV)集群往往面临建模不精确带来的编队控制难题，该文提出一种基于数据驱动的四旋翼无人机集群滑模抗扰编队控制方法。首先，根据四旋翼无人机及其邻居节点的输入输出状态，采用动态线性化方法建立了无人机集群数据驱动编队模型；其次，基于数据驱动模型设计了扩张状态观测器与积分滑模编队控制器，用于对不确定干扰进行在线估计与滑模抗扰编队；最后，对基于数据驱动的无人机集群滑模抗扰编队系统进行了稳定性分析，得到了只需通信拓扑连通和扰动有界的编队稳定性条件。Gazebo仿真与实验结果均表明，所提策略在无人机模型未知且在7 m/s风速的不确定扰动下，集群编队误差优于0.1 m，较传统基于模型控制的四旋翼无人机编队方法和现有数据驱动方法编队误差降低了41%，编队响应时间缩短了40%。
- 四旋翼无人机集群 /
- 数据驱动 /
- 滑模控制
Abstract: Objective Quadrotor Unmanned Aerial Vehicle (UAV) cooperative formation can increase payload capacity and extend the operational range. However, quadrotor UAVs are highly nonlinear and underactuated systems. Differences in size and actuator hardware further weaken the effectiveness of model-based formation-control methods. Therefore, disturbance-rejection formation control is needed for quadrotor UAV swarms with unknown internal models and uncertain external disturbances. Methods To address the difficulty of precise modeling for quadrotor UAV swarm formation under uncertain disturbances, this paper proposes a data-driven sliding-mode disturbance-rejection formation control method. First, a data-driven formation-control model is established using the input and output states of each UAV and its neighboring UAVs. Then, an extended state observer and an integral sliding-mode formation controller are designed to estimate uncertain disturbances online and achieve robust formation control. Finally, stability analysis is conducted to derive sufficient conditions under which all UAVs achieve sliding-mode disturbance-rejection formation. The proposed method is verified through simulations and experiments under an unknown system model and uncertain disturbances. Results and Discussions The simulation results show that multiple quadrotor UAVs can maintain the desired formation geometry in a wind-disturbed environment (Fig. 4). The formation position error converges to within 0.1 m in 15 s and reconverges rapidly after a 7 m/s gust is applied (Fig. 6). The velocity curves also show rapid convergence among the UAVs (Fig. 5). The experimental results indicate that three UAVs can follow the trajectory of the virtual leader while maintaining the desired triangular formation (Fig. 17). The formation error is mostly kept within 0.1 m (Fig. 18). When the observation matrix fluctuates strongly between 10 s and 20 s, the corresponding formation error is relatively large. When the observation matrix curve becomes smoother between 20 s and 30 s, the formation error also decreases (Fig. 20). Compared with traditional model-based formation-control methods and existing data-driven methods, the proposed method reduces the formation error by 41% and shortens the formation response time by 40%. Conclusions This paper proposes a data-driven sliding-mode disturbance-rejection formation control method for quadrotor UAV swarms with unknown internal models and uncertain external disturbances. Under an unknown quadrotor UAV model and a 7 m/s wind disturbance, the proposed method keeps the formation error below 0.1 m. It also reduces the formation error by 41% and shortens the formation response time by 40% compared with traditional model-based formation-control methods and existing data-driven methods. Future work will study multilayer data-driven formation control for heterogeneous UAV-UGV swarm systems. It will also optimize computational cost and scalability in large-scale and complex application scenarios.
- Quadrotor UAV swarm /
- Data-driven formation-control /
- Sliding mode control

HTML全文

图 1 四旋翼无人机集群编队示意图

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图 2 四旋翼无人机集群数据驱动编队控制框架

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图 3 编队通信拓扑与Gazebo仿真环境

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图 4 无人机编队轨迹

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图 5 无人机速度曲线

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图 6 无人机编队误差

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图 7 观测矩阵 $ {\hat{\overline{\boldsymbol{\varPhi }}}}_{i}({t}_{k}) $演化曲线

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图 8 无人机集群规模化编队轨迹

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图 9 无人机集群规模化编队误差

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图 10 无人机编队轨迹

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图 11 无人机编队位移误差

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图 12 观测矩阵$ {\hat{\overline{\boldsymbol{\varPhi }}}}_{i}({t}_{k}) $演化曲线

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图 13 基于模型控制的四旋翼无人机集群编队轨迹及误差演化

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图 14 基于数据驱动方法的四旋翼无人机集群编队轨迹及误差演化

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图 15 无人机编队实验平台

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图 16 无人机编队实验场景

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图 17 无人机编队轨迹

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图 20 观测矩阵$ {\hat{\overline{{\boldsymbol{\varPhi}} }}}_{i}({t}_{k}) $演化曲线

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图 18 无人机编队误差

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图 19 无人机速度曲线

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图 21 已有数据驱动方法实验环境编队轨迹

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图 22 已有数据驱动方法实验环境编队误差

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表 1 不同风扰场景下编队误差(m)

风扰均值(m/s)	t=2 s	t=5 s	t=10 s	t=30 s	t=50 s	t=70 s
7	1.842	0.76	0.098	0.089	0.085	0.082
10	2.01	1.136	0.138	0.075	0.086	0.088
13	1.91	1.49	0.152	0.078	0.084	0.096
15	2.59	1.785	0.236	0.143	0.118	0.125

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