1. 北京理工大学 空天科学与技术学院, 北京 100081
2. 北京理工大学 先进结构技术研究院, 北京 100081
3. 北京信息科技大学 自动化学院, 北京 100192
*wycc@bit.edu.cn
收稿:2024-09-19,
网络出版:2025-08-28,
纸质出版:2025-08-31
移动端阅览
李俊辉, 王伟, 王雨辰, 等. 基于预设时间一致性理论的无人机编队控制[J]. 兵工学报, 2025,46(8):240863.
Junhui LI, Wei WANG, Yuchen WANG, et al. Unmanned Aerial Vehicle Formation Control Based on Prescribed-time Consensus Theory[J]. Acta Armamentarii, 2025, 46(8): 240863.
李俊辉, 王伟, 王雨辰, 等. 基于预设时间一致性理论的无人机编队控制[J]. 兵工学报, 2025,46(8):240863. DOI: 10.12382/bgxb.2024.0863.
Junhui LI, Wei WANG, Yuchen WANG, et al. Unmanned Aerial Vehicle Formation Control Based on Prescribed-time Consensus Theory[J]. Acta Armamentarii, 2025, 46(8): 240863. DOI: 10.12382/bgxb.2024.0863.
无人机编队可以实现复杂的集体任务
降低单个无人机的风险和操作难度。针对三维场景下的无人机集群编队控制问题
基于预设时间稳定理论和多智能体一致性理论
设计领-从结构下的分布式编队复合控制方法。通过分析无人机实际输入与等效控制之间的关系
建立多机运动学模型。为提高无人机对外部扰动的鲁棒性
结合自抗扰控制理论设计一种预设时间收敛扩张状态观测器
实现对扰动的在线估计。进一步
考虑到只有与领航者连通的跟随者能获取领航者的状态信息
提出一种预设时间收敛的分布式估计器
以快速估计领航者的状态。结合观测器及估计器的输出
提出一种预设时间收敛的一致性编队控制协议
并通过Lyapunov理论证明闭环系统的预设时间稳定性。通过构建数值仿真实验
验证了新方法的有效性。研究结果表明
新设计的控制方法在存在外部扰动的情况下可以实现预设时间内稳定的无人机编队协同控制。
Unmanned aerial vehicle (UAV) formation can execute complex collective tasks and reduce the risk and operation difficulty of a single UAV. A distributed formation compound control method with the leader-follower structure is designed based on the prescribed-time stability theory and multi-agent consensus theory for the control of UAV swarm formation in three-dimensional scene.Firstly
a multi-UAVs kinematic model is established by analyzing the relationship between the actual input and the equivalent control.In order to enhance the robustness of UAVs against external disturbances
a prescribed-time convergent extended state observer is designed to achieve online estimation of disturbance based on active disturbance rejection control theory.Furthermore
considering that only the followers connecting to the leader can access the leader's state information
a prescribed-time convergent distributed estimator is introduced to rapidly estimate the leader's state.On this basis
a prescribed-time convergent consensus formation control algorithm is proposed combined with the outputs of the observer and the estimator
and the prescribed-time stability of closed-loop system is proved by Lyapunov theory.The simulated results validate the effectiveness of the proposed method.The research results show that the proposed control method can achieve the stable cooperative control of UAV formation within a preset time in the presence of external disturbances.
曾照洋 , 彭文胜 , 李云凯 , 等 . 智能无人机集群可靠性技术内涵、发展及挑战 [J ] . 兵工学报 , 2025 , 46 ( 3 ): 240322 . DOI: 10.12382/bgxb.2024.0322 http://doi.org/10.12382/bgxb.2024.0322 随着无人机相关技术的快速发展,智能无人机集群得到越来越多的研究。智能无人机集群应用自然界集群组织算法模型,并结合其他智能技术,形成高级群体智能行为,具有传统单人工智能体无法比拟的优势,但是可靠性问题也是制约其面向大规模应用的一个重要因素。系统分析智能无人机集群的概念、特征及技术发展路径,基于智能无人机集群的应用场景、应用模式及行为过程,探讨智能无人机集群的可靠性内涵,构建无人机集群可靠性技术框架,并结合智能无人机集群的技术特征和应用需求,给出智能无人机集群可靠性技术发展的技术途径与建议。最终在“1个核心,2种能力,N个基础”的可靠性内涵下,提出由可靠性指标、可靠性研制验证以及可靠性维持保障技术组成的智能UAVS可靠性技术框架。
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潘无为 , 姜大鹏 , 庞永杰 , 等 . 人工势场和虚拟结构相结合的多水下机器人编队控制 [J ] . 兵工学报 , 2017 , 38 ( 2 ): 326 - 334 . DOI: 10.3969/j.issn.1000-1093.2017.02.017 http://doi.org/10.3969/j.issn.1000-1093.2017.02.017 传统的多水下机器人(AUV)编队算法,例如领航跟随法、虚拟结构法,对编队形成过程中AUV间的避碰问题和编队行进过程中的避障问题,没有进行有效解决。人工势场法可利用势函数进行避碰、避障,但队形组织能力略显不足。针对上述问题,提出了一种人工势场和虚拟结构相结合的多AUV编队控制算法。将系统分为3个部分:编队参考点、虚拟结构质点和AUV. 以编队参考点为中心形成期望的虚拟结构以组织队形;虚拟结构质点以期望的虚拟结构为运动目标,并在运动的过程中,通过人工势场斥函数,实现避碰和避障;AUV对虚拟结构质点进行目标跟踪,从而渐进形成AUV的队形。通过编队路径跟踪、编队队形变换和编队避障等一系列仿真实验,对算法的可靠性和灵活性进行充分验证。实验结果表明:在随机的初始位置条件下,多AUV系统可以快速无碰撞地形成队形;在编队行进过程中进行灵活的队形变换,并对障碍物有效避碰。
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ZOU Y , MENG Z Y . Adaptive formation control of multiple vertical takeoff and landing UAVs [J ] . Scientia Sinica(Technologica) , 2020 , 50 ( 4 ): 369 - 379 . (in Chinese)
张清瑞 , 刘赟韵 , 孙慧杰 , 等 . 固定翼无人机紧密编队的鲁棒协同跟踪控制 [J ] . 航空学报 , 2024 , 45 ( 1 ): 629233 . DOI: 10.7527/S1000-6893.2023.29233 http://doi.org/10.7527/S1000-6893.2023.29233 固定翼无人机(UAV)具有典型的欠驱动、非线性等特点,导致航点自主跟踪设计难度大,同时紧密编队飞行过程中无人机之间运动耦合与气动干扰明显,进一步增加了高性能控制设计难度。为此,本文针对固定翼无人机紧密编队飞行的航点自主跟踪问题,综合考虑轨迹平滑性、运动协同性和跟踪鲁棒性等需求,依托全驱系统建模方法提出了一种多层次鲁棒协同跟踪控制架构。该架构包括上层运动规划、中层协同滤波及底层鲁棒协同跟踪控制3个核心部分。上层运动规划根据离散航点指令,融合虚拟结构法和迭代线性二次型优化,实时生成可行、平滑的运动轨迹。在此基础上,为改善跟踪控制的瞬态性能,引入分布式协同滤波思想,对规划轨迹进行滤波处理,生成每架无人机个体的参考信号。最后,考虑到紧密编队中气动耦合与系统不确定性等挑战,设计了一种基于不确定性及干扰观测器的鲁棒协同跟踪控制方法,实现了编队轨迹的精确跟踪以及队形的可靠保持。所提出的鲁棒协同跟踪控制架构考虑了紧密编队飞行中的多种约束与挑战,实现了编队运动规划与跟踪控制的综合设计,可以有效提升系统自主性、协同性和鲁棒性。最后,通过5架固定翼无人机的紧密编队飞行仿真对所提方法进行综合测试,验证了方法的有效性。
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