Consensus based on learning game theory with a UAV rendezvous application

作     者：Lin Zhongjie Liu Hugh hong-tao 

作者机构：University of Toronto Institute for Aerospace Studies 4925 Dufferin StreetToronto Ontario M3H 5T6 Canada 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2015年第28卷第1期

页      面：191-199页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 082503[工学-航空宇航制造工程] 0835[工学-软件工程] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：funded by China Scholarship Council(No201206230108) the Natural Sciences and Engineering Research Council of Canada Discovery Grant(No.RGPIN227674) 

主　　题：cooperation consensus robustness autonomy attractive military behaviour contributions proof centralized 

摘      要：Multi-agent cooperation problems are becoming more and more attractive in both civilian and military applications. In multi-agent cooperation problems, different network topologies will decide different manners of cooperation between agents. A centralized system will directly control the operation of each agent with information flow from a single centre, while in a distributed system, agents operate separately under certain communication protocols. In this paper, a systematic distributed optimization approach will be established based on a learning game *** convergence of the algorithm will be proven under the game theory framework. Two typical consensus problems will be analyzed with the proposed algorithm. The contributions of this work are threefold. First, the designed algorithm inherits the properties in learning game theory for problem simplification and proof of convergence. Second, the behaviour of learning endows the algorithm with robustness and autonomy. Third, with the proposed algorithm, the consensus problems will be analyzed from a novel perspective.