Abolfazl Jalilvand, A. Kimiyaghalam, A. Ashouri, M. Mahdavi
{"title":"基于先进粒子群优化的PID控制器参数整定","authors":"Abolfazl Jalilvand, A. Kimiyaghalam, A. Ashouri, M. Mahdavi","doi":"10.1109/INMIC.2008.4777776","DOIUrl":null,"url":null,"abstract":"PID parameter optimization is an important problem in control field. Particle swarm optimization (PSO) is powerful stochastic evolutionary algorithm that is used to find the global optimum solution in search space. However, it has been observed that the standard PSO algorithm has premature and local convergence phenomenon when solving complex optimization problem. To resolve this problem an advanced particle swarm optimization (APSO) is proposed in this paper. This new algorithm is proposed to augment the original PSO searching speed. This study proposes to use the (APSO) for its fast searching speed. These advanced particle swarm optimization to accelerate the convergence. The algorithms are simulated with MATLAB programming. The simulation result shows that the PID controller with (APSO) has a fast convergence rate and a better dynamic performance.","PeriodicalId":112530,"journal":{"name":"2008 IEEE International Multitopic Conference","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":"{\"title\":\"Advanced particle swarm optimization-based PID controller parameters tuning\",\"authors\":\"Abolfazl Jalilvand, A. Kimiyaghalam, A. Ashouri, M. Mahdavi\",\"doi\":\"10.1109/INMIC.2008.4777776\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"PID parameter optimization is an important problem in control field. Particle swarm optimization (PSO) is powerful stochastic evolutionary algorithm that is used to find the global optimum solution in search space. However, it has been observed that the standard PSO algorithm has premature and local convergence phenomenon when solving complex optimization problem. To resolve this problem an advanced particle swarm optimization (APSO) is proposed in this paper. This new algorithm is proposed to augment the original PSO searching speed. This study proposes to use the (APSO) for its fast searching speed. These advanced particle swarm optimization to accelerate the convergence. The algorithms are simulated with MATLAB programming. The simulation result shows that the PID controller with (APSO) has a fast convergence rate and a better dynamic performance.\",\"PeriodicalId\":112530,\"journal\":{\"name\":\"2008 IEEE International Multitopic Conference\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"25\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Multitopic Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INMIC.2008.4777776\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Multitopic Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INMIC.2008.4777776","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PID parameter optimization is an important problem in control field. Particle swarm optimization (PSO) is powerful stochastic evolutionary algorithm that is used to find the global optimum solution in search space. However, it has been observed that the standard PSO algorithm has premature and local convergence phenomenon when solving complex optimization problem. To resolve this problem an advanced particle swarm optimization (APSO) is proposed in this paper. This new algorithm is proposed to augment the original PSO searching speed. This study proposes to use the (APSO) for its fast searching speed. These advanced particle swarm optimization to accelerate the convergence. The algorithms are simulated with MATLAB programming. The simulation result shows that the PID controller with (APSO) has a fast convergence rate and a better dynamic performance.