基于虚拟管的微型多旋翼无人机分布式模型预测编队控制

IF 5.7 2区 计算机科学 Q1 ENGINEERING, AEROSPACE IEEE Transactions on Aerospace and Electronic Systems Pub Date : 2025-03-07 DOI:10.1109/TAES.2025.3549005
Yong Chen;Jieyuan Yang;Xunhua Dai;Qianyue Luo;Fuxi Niu
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引用次数: 0

摘要

针对微型多旋翼无人机的编队探测问题,提出了一种基于虚拟管的分布式方法。建立了虚拟管的通用数学模型。然后,介绍了一种基于分布式模型预测控制和一致性理论的编队簇路径规划方法。该方法通过将管约束建模为具有线性约束的多目标优化问题,最终将所考虑的问题转化为二次规划问题,从而在计算资源有限的实际无人机机载计算机上部署。此外,为了提高算法的稳定性,提出了一种基于邻居预测信息的假设状态构造器,从而消除了对所有无人机目标点的先验知识的需要。最后,通过三种不同管道环境的仿真和六架多旋翼无人机的实际实验,验证了该算法的有效性和实用性。
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Distributed Model Predictive Formation Control of Micro Multirotor UAVs With Virtual Tube
A distributed approach based on virtual tube is proposed in this article to address the formation exploration of micro multirotor unmanned aerial vehicles (UAVs). A generic mathematical model for the virtual tube is established. Then, a path planning method for a formation cluster based on distributed model predictive control and the consensus theory is introduced. This method incorporates tube constraints by modeling the problem as a multiobjective optimization with linear constraints, then the considered problem is ultimately transformed into a quadratic programming problem that can be deployed on real UAV's onboard computers with limited computational resources. In addition, an assumed state constructor based on neighbor prediction information is proposed to enhance algorithm stability, thus eliminating the need for a priori knowledge of target points for all UAVs. Finally, the effectiveness and practicality of the algorithm are validated through simulations in three different tube environments and a real-world experiment involving six multirotor UAVs.
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来源期刊
CiteScore
7.80
自引率
13.60%
发文量
433
审稿时长
8.7 months
期刊介绍: IEEE Transactions on Aerospace and Electronic Systems focuses on the organization, design, development, integration, and operation of complex systems for space, air, ocean, or ground environment. These systems include, but are not limited to, navigation, avionics, spacecraft, aerospace power, radar, sonar, telemetry, defense, transportation, automated testing, and command and control.
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