{"title":"基于粒子群算法的PID控制器在线自整定","authors":"Xuzhou Li, Fei Yu, You-bo Wang","doi":"10.1109/CIS.2007.194","DOIUrl":null,"url":null,"abstract":"Proportional-Integral-Derivative (PID) controller is still widely used in control engineering, and tuning of PID is a crucial operation. We utilize particle swarm optimization algorithm to design an online self- tuning framework of PID controller. Our system is simulated in Matlab based on particle swarm optimi- zation algorithm. Experiment focus on several prob- lems application concerned. Our conclusions include that different fitness function can lead to different time response, and application system should initialize range of each particle as small as possible. Moreover, the conclusions also include that we should choose a modest generations for the online system with linearly inertia weight consume less times evolutionary genera- tion, not a larger one. These conclusions can contrib- ute mostly to application system concerning about cal- culation cost. Keywords: PSO, PID controller, Matlab","PeriodicalId":127238,"journal":{"name":"2007 International Conference on Computational Intelligence and Security (CIS 2007)","volume":"20 9","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"35","resultStr":"{\"title\":\"PSO Algorithm Based Online Self-Tuning of PID Controller\",\"authors\":\"Xuzhou Li, Fei Yu, You-bo Wang\",\"doi\":\"10.1109/CIS.2007.194\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Proportional-Integral-Derivative (PID) controller is still widely used in control engineering, and tuning of PID is a crucial operation. We utilize particle swarm optimization algorithm to design an online self- tuning framework of PID controller. Our system is simulated in Matlab based on particle swarm optimi- zation algorithm. Experiment focus on several prob- lems application concerned. Our conclusions include that different fitness function can lead to different time response, and application system should initialize range of each particle as small as possible. Moreover, the conclusions also include that we should choose a modest generations for the online system with linearly inertia weight consume less times evolutionary genera- tion, not a larger one. These conclusions can contrib- ute mostly to application system concerning about cal- culation cost. Keywords: PSO, PID controller, Matlab\",\"PeriodicalId\":127238,\"journal\":{\"name\":\"2007 International Conference on Computational Intelligence and Security (CIS 2007)\",\"volume\":\"20 9\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"35\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 International Conference on Computational Intelligence and Security (CIS 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIS.2007.194\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 International Conference on Computational Intelligence and Security (CIS 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIS.2007.194","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PSO Algorithm Based Online Self-Tuning of PID Controller
Proportional-Integral-Derivative (PID) controller is still widely used in control engineering, and tuning of PID is a crucial operation. We utilize particle swarm optimization algorithm to design an online self- tuning framework of PID controller. Our system is simulated in Matlab based on particle swarm optimi- zation algorithm. Experiment focus on several prob- lems application concerned. Our conclusions include that different fitness function can lead to different time response, and application system should initialize range of each particle as small as possible. Moreover, the conclusions also include that we should choose a modest generations for the online system with linearly inertia weight consume less times evolutionary genera- tion, not a larger one. These conclusions can contrib- ute mostly to application system concerning about cal- culation cost. Keywords: PSO, PID controller, Matlab