[1]ZHANG Tianjie,DUAN Haibin.A modified consensus algorithm for multi-UAV formations based on pigeon-inspired optimization with a slow diving strategy[J].智能系统学报,2017,12(4):570-581.[doi:10.11992/tis.201604006]
ZHANG Tianjie,DUAN Haibin.A modified consensus algorithm for multi-UAV formations based on pigeon-inspired optimization with a slow diving strategy[J].CAAI Transactions on Intelligent Systems,2017,12(4):570-581.[doi:10.11992/tis.201604006]
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ZHANG Tianjie,DUAN Haibin.A modified consensus algorithm for multi-UAV formations based on pigeon-inspired optimization with a slow diving strategy[J].CAAI Transactions on Intelligent Systems,2017,12(4):570-581.[doi:10.11992/tis.201604006]
A modified consensus algorithm for multi-UAV formations based on pigeon-inspired optimization with a slow diving strategy
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
12
期数:
2017年第4期
页码:
570-581
栏目:
学术论文—智能系统
出版日期:
2017-08-25
- Title:
- A modified consensus algorithm for multi-UAV formations based on pigeon-inspired optimization with a slow diving strategy
- 关键词:
- unmanned aerial vehicle (UAV); formation; consensus; pigeon-inspired optimization (PIO); Banach fixed-point theorem
- Keywords:
- unmanned aerial vehicle (UAV); formation; consensus; pigeon-inspired optimization (PIO); Banach fixed-point theorem
- 分类号:
- TP18;TP273
- 摘要:
- This paper considers the formation control problem for a group of unmanned aerial vehicles（UAVs） employing consensus with different optimizers.A group of UAVs can never accomplish difficult tasks without formation because if disordered they do not work any better than a single vehicle，and a single vehicle is limited by its undeveloped intelligence and insufficient load.Among the many formation methods，consensus has attracted much attention because of its effectiveness and simplicity.However，at the beginning of convergence，overshoot and oscillation are universal because of the limitation of communication and a lack of forecasting，which are inborn shortcomings of consensus.It is natural to modify this method with lots of optimizers.In order to reduce overshoot and smooth trajectories，this paper first adopted particle swarm optimization（PSO），then pigeon-inspired optimization（PIO） to modify the consensus.PSO is a very popular optimizer，while PIO is a new method，both work but still retain disadvantages such as residual oscillation.As a result，it was necessary to modify PIO，and a pigeon-inspired optimization with a slow diving strategy（SD-PIO） is proposed.Convergence analysis was performed on the SD-PIO based on the Banach fixed-point theorem and conditions sufficient for stability were achieved.Finally，a series of comparative simulations were conducted to verify the feasibility and effectiveness of the proposed approach.
- Abstract:
- This paper considers the formation control problem for a group of unmanned aerial vehicles (UAVs) employing consensus with different optimizers.A group of UAVs can never accomplish difficult tasks without formation because if disordered they do not work any better than a single vehicle,and a single vehicle is limited by its undeveloped intelligence and insufficient load.Among the many formation methods,consensus has attracted much attention because of its effectiveness and simplicity.However,at the beginning of convergence,overshoot and oscillation are universal because of the limitation of communication and a lack of forecasting,which are inborn shortcomings of consensus.It is natural to modify this method with lots of optimizers.In order to reduce overshoot and smooth trajectories,this paper first adopted particle swarm optimization (PSO),then pigeon-inspired optimization (PIO) to modify the consensus.PSO is a very popular optimizer,while PIO is a new method,both work but still retain disadvantages such as residual oscillation.As a result,it was necessary to modify PIO,and a pigeon-inspired optimization with a slow diving strategy (SD-PIO) is proposed.Convergence analysis was performed on the SD-PIO based on the Banach fixed-point theorem and conditions sufficient for stability were achieved. Finally,a series of comparative simulations were conducted to verify the feasibility and effectiveness of the proposed approach.
- 参考文献/References:
-
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备注/Memo
收稿日期:2017-04-11。
基金项目:Natural Science Foundation of China under Grant（61333004）.
作者简介:ZHANG Tianjie was born in 1992.He received his Bachelor’s degree from BUAA in 2015 and now he is a graduate student in BUAA.His main research focuses on aircraft mission planning;DUAN Haibin was born in Shandong,China,in 1976.He received the Ph.D.degree from Nanjing University of Aeronautics and Astronautics,Nanjing,China,in 2005.He is currently a Full Professor with the School of Automation Science and Electrical Engineering,Beihang University (formerly Beijing University of Aeronautics and Astronautics),Beijing,China,where he is the Head of the Bio-inspired Autonomous Flight Systems Research Group.He has authored or coauthored more than 70 publications.His research interests are multiple unmanned-aerial-vehicle autonomous formation control and biological computer vision.
通讯作者:ZHANG Tianjie,E-mail:11031148@buaa.edu.cn.
更新日期/Last Update:
2017-08-25