{"title":"A Game Theoretic Based Biologically-Inspired Distributed Intelligent Flocking Control for Multi-UAV Systems with Network Imperfections","authors":"Mohammad Jafari, Hao Xu","doi":"10.1109/SSCI.2018.8628814","DOIUrl":null,"url":null,"abstract":"In this paper, a game theoretic based biologically-inspired distributed intelligent control methodology is proposed to overcome challenges in networked multi-UAV, i.e., networked imperfections and uncertainty from environment and system. Considering the limited computational ability in the practical onboard micro-controller, the proposed method is adopted based on the game theory, and the emotional learning phenomenon in the mammalian limbic system. The learning capability and low computational complexity of the proposed technique makes it a propitious tool for implementing in networked multi-UAV flocking even in presence of the network imperfections and uncertainty from environment and system. Lyapunov analysis and computer-aid numerical simulation results of the implementation of the proposed methodology demonstrate the effectiveness of this algorithm.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSCI.2018.8628814","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, a game theoretic based biologically-inspired distributed intelligent control methodology is proposed to overcome challenges in networked multi-UAV, i.e., networked imperfections and uncertainty from environment and system. Considering the limited computational ability in the practical onboard micro-controller, the proposed method is adopted based on the game theory, and the emotional learning phenomenon in the mammalian limbic system. The learning capability and low computational complexity of the proposed technique makes it a propitious tool for implementing in networked multi-UAV flocking even in presence of the network imperfections and uncertainty from environment and system. Lyapunov analysis and computer-aid numerical simulation results of the implementation of the proposed methodology demonstrate the effectiveness of this algorithm.