DocumentCode
1795428
Title
Pigeon-Inspired optimization approach to multiple UAVs formation reconfiguration controller design
Author
Xiaomin Zhang ; Haibin Duan ; Chen Yang
Author_Institution
Sch. of Adv. Eng., Beihang Univ., Beijing, China
fYear
2014
fDate
8-10 Aug. 2014
Firstpage
2707
Lastpage
2712
Abstract
This article introduces a controller for solving the problem of multiple unmanned aerial vehicles (UAVs) formation reconfiguration. Under the constraints of terminal status and of control action energy, the controller aims to find the best values of UAV´s inputs (including thrust, load factor, bank angle) to accomplish the task. The basis of our controller is a method which combines PIO with CPTD. Pigeon-Inspired optimization (PIO) is a novel algorithm. It has been proposed and applied in engineering problems by following the inspirational precious works. CPTD is a method called control parameterization and time discretization. Besides, we use a mathematical model to get a function which can evaluate the reconfiguration effects. Finally, to verify the validity of our controller, comparative experiments between PIO and particle swarm optimization (PSO) are conducted. The comparative results demonstrate that our proposed controller embedded with PIO is much better than the one with PSO.
Keywords
autonomous aerial vehicles; control system synthesis; evolutionary computation; multi-robot systems; particle swarm optimisation; position control; PIO algorithm; PSO; UAV formation reconfiguration controller design; control action energy constraint; control parameterization; multiple unmanned aerial vehicles; particle swarm optimization; pigeon-inspired optimization approach; reconfiguration effect; terminal status constraint; time discretization; Compass; Educational institutions; Equations; Linear programming; Mathematical model; Solid modeling; Three-dimensional displays;
fLanguage
English
Publisher
ieee
Conference_Titel
Guidance, Navigation and Control Conference (CGNCC), 2014 IEEE Chinese
Conference_Location
Yantai
Print_ISBN
978-1-4799-4700-3
Type
conf
DOI
10.1109/CGNCC.2014.7007594
Filename
7007594
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