{"title":"Hybrid Intelligent Formation Control Using PSO_GEN","authors":"Mehdi J. Marie, S. S. Mahdi, Esraa Y. Yahia","doi":"10.1109/DeSE.2019.00129","DOIUrl":null,"url":null,"abstract":"In this paper , hybrid intelligent formation control by using particle swarm optimization and genetic algorithms is studied .,Firstly a survey about the formation control is presented .Then a close loop system is selected to be controlled by optimized PID controller. The PID optimized in three methods which are particle swarm optimization , genetic algorithm and combination of PSO and genetic algorithms .The fitness function used in all three cases is integral square error (ISE) .Different swarm numbers and steps are used for comparison .The simulation results show that combination method has a better performance than both PSO and GA as it produces minimum error.","PeriodicalId":6632,"journal":{"name":"2019 12th International Conference on Developments in eSystems Engineering (DeSE)","volume":"98 1","pages":"693-698"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 12th International Conference on Developments in eSystems Engineering (DeSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DeSE.2019.00129","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper , hybrid intelligent formation control by using particle swarm optimization and genetic algorithms is studied .,Firstly a survey about the formation control is presented .Then a close loop system is selected to be controlled by optimized PID controller. The PID optimized in three methods which are particle swarm optimization , genetic algorithm and combination of PSO and genetic algorithms .The fitness function used in all three cases is integral square error (ISE) .Different swarm numbers and steps are used for comparison .The simulation results show that combination method has a better performance than both PSO and GA as it produces minimum error.