{"title":"基于粒子群算法的多模型自适应预测控制","authors":"Liu Gui-ying, Qu Li-ping, Liu Yun-feng","doi":"10.1109/DCABES.2017.36","DOIUrl":null,"url":null,"abstract":"In terms of the characteristics of time lag system, the method of multiple models adaptive predictive control based on particle swarm optimization (PSO) algorithm is proposed. Multiple Models can approach the dynamic character of the controlled object. We can design corresponding controller to each model. We can get final controlled variable with the limited controlled variable by means of weighting. Each controller adopts Predictive Control method. At last the method gets the global optimum by PSO algorithm. We compare the method to PID in time lag system. Simulation result indicates the method not only can overcome inaccuracy of modeling and time variation of parameters but also has good control performance and stronger robustness.","PeriodicalId":446641,"journal":{"name":"2017 16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multiple Models Adaptive Predictive Control Based on PSO Algorithm\",\"authors\":\"Liu Gui-ying, Qu Li-ping, Liu Yun-feng\",\"doi\":\"10.1109/DCABES.2017.36\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In terms of the characteristics of time lag system, the method of multiple models adaptive predictive control based on particle swarm optimization (PSO) algorithm is proposed. Multiple Models can approach the dynamic character of the controlled object. We can design corresponding controller to each model. We can get final controlled variable with the limited controlled variable by means of weighting. Each controller adopts Predictive Control method. At last the method gets the global optimum by PSO algorithm. We compare the method to PID in time lag system. Simulation result indicates the method not only can overcome inaccuracy of modeling and time variation of parameters but also has good control performance and stronger robustness.\",\"PeriodicalId\":446641,\"journal\":{\"name\":\"2017 16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)\",\"volume\":\"86 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DCABES.2017.36\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DCABES.2017.36","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multiple Models Adaptive Predictive Control Based on PSO Algorithm
In terms of the characteristics of time lag system, the method of multiple models adaptive predictive control based on particle swarm optimization (PSO) algorithm is proposed. Multiple Models can approach the dynamic character of the controlled object. We can design corresponding controller to each model. We can get final controlled variable with the limited controlled variable by means of weighting. Each controller adopts Predictive Control method. At last the method gets the global optimum by PSO algorithm. We compare the method to PID in time lag system. Simulation result indicates the method not only can overcome inaccuracy of modeling and time variation of parameters but also has good control performance and stronger robustness.