GuoDong Li, Chen-Hong Wang, S. Masuda, D. Yamaguchi, M. Nagai
{"title":"The PID prediction control system using particle swarm optimization and genetic algorithms","authors":"GuoDong Li, Chen-Hong Wang, S. Masuda, D. Yamaguchi, M. Nagai","doi":"10.1109/GSIS.2009.5408225","DOIUrl":null,"url":null,"abstract":"In this paper, the particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are employed to optimize the parameters of PID algorithm in order to improve the performance of PID control system. Moreover, we propose the grey model based on grey system theory to combine with PID control to establish the PID prediction control system. The proposed control system can realize the accurate control in realtime. Finally, we validated the effectiveness of the proposed control system by computer simulation.","PeriodicalId":294363,"journal":{"name":"2009 IEEE International Conference on Grey Systems and Intelligent Services (GSIS 2009)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Grey Systems and Intelligent Services (GSIS 2009)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GSIS.2009.5408225","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, the particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are employed to optimize the parameters of PID algorithm in order to improve the performance of PID control system. Moreover, we propose the grey model based on grey system theory to combine with PID control to establish the PID prediction control system. The proposed control system can realize the accurate control in realtime. Finally, we validated the effectiveness of the proposed control system by computer simulation.