{"title":"A performance-dependent PSO based optimization of PID controller for DC motor","authors":"H. Verma, M. C. Jain","doi":"10.1109/ICEES.2011.5725327","DOIUrl":null,"url":null,"abstract":"This paper proposed a new approach to control speed of linear brushless DC motor. This paper provides an overview of performance dependant particle swarm optimization (PDPSO) and presenting it as an alternative to evolutionary algorithm. Performance Dependent Particle swarm optimization is used to determine optimal gains of proportional-derivative-integral controller (PID). To obtain optimal solutions, PDPSO introduced the relationship between particle selection and particles performance. The proposed method shows its robustness under critical conditions when conventional optimization methods fail.","PeriodicalId":156837,"journal":{"name":"2011 1st International Conference on Electrical Energy Systems","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 1st International Conference on Electrical Energy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEES.2011.5725327","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16
Abstract
This paper proposed a new approach to control speed of linear brushless DC motor. This paper provides an overview of performance dependant particle swarm optimization (PDPSO) and presenting it as an alternative to evolutionary algorithm. Performance Dependent Particle swarm optimization is used to determine optimal gains of proportional-derivative-integral controller (PID). To obtain optimal solutions, PDPSO introduced the relationship between particle selection and particles performance. The proposed method shows its robustness under critical conditions when conventional optimization methods fail.