通讯受限下无人机集群自适应追踪控制

doi:10.12305/j.issn.1001-506X.2025.10.24

系统工程与电子技术 ›› 2025, Vol. 47 ›› Issue (10): 3401-3410.doi: 10.12305/j.issn.1001-506X.2025.10.24

• 制导、导航与控制 • 上一篇

通讯受限下无人机集群自适应追踪控制

张佳龙¹, 赵迪², 张普³, 叶磊⁴^,*

1. 国防科技大学空天科学学院，湖南长沙 410073
2. 中国科学院数学与系统科学研究院，北京 100190
3. 西安理工大学自动化与信息工程学院，陕西西安 710048
4. 国防科技大学试验训练基地，陕西西安 710106

收稿日期:2024-05-23 出版日期:2025-10-25 发布日期:2025-10-23
通讯作者: 叶磊
作者简介:张佳龙（1990—），男，工程师，博士后，主要研究方向为集群控制、空中联合作战闭合杀伤链
赵　迪（1991—），女，工程师，硕士，主要研究方向为无人智能集群作战
张　普（1990—），女，讲师，博士，主要研究方向为多智能体协同容错控制
基金资助:
陕西省自然科学基础研究计划（2024JC-YBQN-0693）；国家博士后研究人员计划（GZC20242273）；国家博士后面上项目（2024MD76018）资助课题

Adaptive tracking control for UAV swarm with communication constraint

Jialong ZHANG¹, Di ZHAO², Pu ZHANG³, Lei YE⁴^,*

1. College of Aerospace Science， National University of Defense Technology， Changsha 410073， China
2. Academy of Mathematics and Systems Science，Chinese Academy of Sciences，Beijing 100190，China
3. School of Automation and Information Engineering，Xi’an University of Technology，Xi’an 710048，China
4. Test Center, National University of Defense Technology, Xi’an 710106, China

Received:2024-05-23 Online:2025-10-25 Published:2025-10-23
Contact: Lei YE

摘要/Abstract

摘要：

影响无人机（unmanned aerial vehicle，UAV）集群追踪效果的最大误差来源之一是通讯过载，因此通讯过载进行实时量化处理将直接影响UAV集群追踪性能。首先，针对无人集群编队敏捷追踪时敏目标，提出一种具有量化的自适应控制方法，该方法能够实现集群在通讯受限下依旧保持预设的追踪效果。其次，考虑部分参数未知的一类高阶非线性系统，采用量化理论建立集群编队模型。然后，针对UAV集群模型，采用变量转换思路，获取完整性约束的集群模型，结合反步技术，设计UAV集群追踪控制律，并实时更新模型未知参数；再次，将所提方法与滑模控制、动态面控制进行仿真对比分析，同时对量化特性进行讨论。最后，通过Lyapunov稳定性理论和UAV样机仿真实验，验证所设计控制器的有效性和合理性，对工程实践具有一定指导意义。

关键词: 通讯受限, 无人机集群编队, 时变轨迹, 追踪控制策略, 期望队形

Abstract:

One of the largest sources of error affecting the tracking effect of unmanned aerial vehicle （UAV） swarms is communication overload, so the real-time quantization processing of communication overload directly affects the cluster tracking performance. Firstly, for agile tracking of time-sensitive targets by unmanned cluster formation, a quantized adaptive control method is proposed, which is able to realize that the cluster maintains the preset tracking effect with communication constraints. Secondly, considering a class of high-order nonlinear systems with unknown parameters, a cluster formation model is established, and quantization theory is introduced. Thirdly, for the UAV cluster model, the variable transformation idea is used to obtain the cluster model with integrity constraints, and combined with backstepping technique, UAV cluster tracking control law is designed, and the unknown parameters of the model are updated in real-time. Then, the proposed method is compared and analyzed with sliding mode control and dynamic surface control in simulation, and the quantitative characteristics are discussed and analyzed at the same time. Finally, the validity and rationality of the designed controller are verified by Lyapunov stability theory and UAV prototype simulation experiments, and the proposed method has a guiding significance for engineering application.

Key words: communication constraint, unmanned aerial vehicle (UAV) swarm formation, time-vary trajectory, tracking control strategy, desired formation shape

中图分类号:

张佳龙, 赵迪, 张普, 叶磊. 通讯受限下无人机集群自适应追踪控制[J]. 系统工程与电子技术, 2025, 47(10): 3401-3410.

Jialong ZHANG, Di ZHAO, Pu ZHANG, Lei YE. Adaptive tracking control for UAV swarm with communication constraint[J]. Systems Engineering and Electronics, 2025, 47(10): 3401-3410.

图/表 12

图1

图2

表1

自动驾驶仪模型相关参数"

无人机	$ \left(x_i(0), y_i(0), z_i(0)\right)$	$ \left( {{\delta _{ix}}\left( t \right),{\delta _{iy}}\left( t \right),{\delta _{iz}}\left( t \right)} \right) $	$ \left(d_{i v}(t), d_{i \theta}(t), \sigma_{i \psi}(t)\right) $
UAV#1	$ \left( { - 1,3.5,0} \right) $	$ \left( {2,2,0} \right) $	$ \left( { - {{\mathrm{e}}^{ - t}}\cos t,1,0} \right) $
UAV#2	$ \left( { - 1,3.6,0} \right) $	$ \left( {0,\tanh (0.5 (t - 20)) - 1,0} \right) $	$ \left( {1.5,{{\mathrm{e}}^{ - t}}\sin (0.5t),0} \right) $
UAV#3	$ \left( { - 1,3.7,0} \right) $	$ \left( { - 1 - \tanh (0.5 (t - 20)),2,0} \right) $	$ \left( { - {{\mathrm{e}}^{ - 2t}},\cos (0.6t),0} \right) $
UAV#4	$ \left( { - 1,3.9,0} \right) $	$ \left( { - 2,\tanh (0.5 (t - 20)) - 3,0} \right) $	$ \left( { - {{\mathrm{e}}^{ - 2t}},\sin (0.6t),0} \right) $
UAV#5	$ \left( { - 1,4.0,0} \right) $	$ \left( { - 1,\tanh (0.5 (t - 20)) - 4,0} \right) $	$ \left( { - {{\mathrm{e}}^{ - 2t}},\cos (0.4t),0} \right) $
UAV#6	$ \left( { - 1,4.1,0} \right) $	$ \left( {1,\tanh (0.5 (t - 20)) - 2,0} \right) $	$ \left( {\cos (0.6t), - {{\mathrm{e}}^{ - 2t}},0} \right) $
UAV#7	$ \left( { - 1,4.2,0} \right) $	$ \left( { - 1,\tanh (0.5 (t - 20)) - 5,0} \right) $	$ \left( { - {{\mathrm{e}}^{ - t}},\sin (0.8t),0} \right) $
UAV#8	$ \left( { - 1,4.3,0} \right) $	$ \left( { - 1,\tanh (0.5 (t - 20)) - 6,0} \right) $	$ \left( { - {{\mathrm{e}}^{ - t}},\sin (0.6t),0} \right) $
UAV#9	$ \left( { - 1,4.8,0} \right) $	$ \left( { - 1,\tanh (0.5 (t - 20)) - 7,0} \right) $	$ \left( { - {{\mathrm{e}}^{ - t}},\sin (0.4t),0} \right) $
UAV#10	$ \left( { - 1,5.0,0} \right) $	$ \left( { - 1,\tanh (0.5 (t - 20)) - 7.5,0} \right) $	$ \left( { - {{\mathrm{e}}^{ - t}},\sin (0.2t),0} \right) $
UAV#11	$ \left( { - 1,5.2,0} \right) $	$ \left( { - 1,\tanh (0.5 (t - 20)) - 8,0} \right) $	$ \left( { - {{\mathrm{e}}^{ - t}},\sin (1.2t),0} \right) $
UAV#12	$ \left( { - 1,5.4,0} \right) $	$ \left( { - 1,\tanh (0.5 (t - 20)) - 8.4,0} \right) $	$ \left( { - {{\mathrm{e}}^{ - t}},\sin (1.4t),0} \right) $
UAV#13	$ \left( { - 1,5.6,0} \right) $	$ \left( { - 1,\tanh (0.5 (t - 20)) - 2.5,0} \right) $	$ \left( { - {{\mathrm{e}}^{ - t}},\sin (1.6t),0} \right) $

表1

图3

图4

图5

图6

图7

图8

图9

图10

图11

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通讯受限下无人机集群自适应追踪控制

Adaptive tracking control for UAV swarm with communication constraint

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