控制与决策2024,Vol.39Issue(11) :3557-3566.DOI:10.13195/j.kzyjc.2023.0945

基于同步DMPC的无人机编队跟踪与避障控制

UAV formation tracking and obstacle avoidance control based on synchronous DMPC

何云风 史贤俊 卢建华 赵超轮 赵国荣
控制与决策2024,Vol.39Issue(11) :3557-3566.DOI:10.13195/j.kzyjc.2023.0945

基于同步DMPC的无人机编队跟踪与避障控制

UAV formation tracking and obstacle avoidance control based on synchronous DMPC

何云风 1史贤俊 1卢建华 1赵超轮 1赵国荣1
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作者信息

  • 1. 海军航空大学,山东烟台 264001
  • 折叠

摘要

针对无人机编队跟踪问题,并考虑对静/动态障碍的规避,提出一种基于同步分布式模型预测控制(DMPC)的编队跟踪与避障控制算法.首先,在DMPC的框架下,以轨迹跟踪和编队保持为目标设计代价函数,并将机间避碰和障碍物规避处理为约束条件;其次,将速度障碍法与避碰、避障以及相容性约束进行融合,并设计终端约束,实现规避动态障碍的功能;再次,根据新的终端约束,以编队稳定为原则,设计与之适配的完整的终端成分;然后,在新的约束和终端成分下,基于Lyapunov理论分析编队系统的稳定性;最后,通过仿真验证所提出算法在编队跟踪和避障方面的有效性和优越性.

Abstract

Aiming at the problem of UAV formation tracking,and considering the avoidance of static and dynamic obstacles,a formation tracking and obstacle avoidance control algorithm based on synchronous distributed model predictive control(DMPC)is proposed.Firstly,within the framework of the DMPC,cost function is designed with trajectory tracking and formation maintenance as the objectives.And collision avoidance and obstacle avoidance are treated as constraint conditions.Secondly,the velocity obstacle(VO)method is integrated with collision avoidance,obstacle avoidance,and compatibility constraints,and terminal constraints are designed to achieve the function of avoiding dynamic obstacles.Thirdly,based on the new terminal constraints,with the principle of ensuring formation system stability,complete terminal components are designed,which are compatible with them.Furthermore,under new constraints and terminal components,the stability of the formation system is analyzed based on the Lyapunov theory.Finally,the effectiveness and superiority of the proposed algorithm in formation tracking and obstacle avoidance are verified through simulation.

关键词

无人机/编队控制/避障/同步分布式模型预测控制/速度障碍法

Key words

UAV/formation control/obstacle avoidance/synchronous DMPC/velocity obstacle method

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出版年

2024
控制与决策
东北大学

控制与决策

CSTPCD北大核心
影响因子:1.227
ISSN:1001-0920
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