{"title":"采用LMI方法的基于模型的T-S模糊预测控制新方法","authors":"A. Zahaf, B. Boutamina, S. Bououden, S. Filali","doi":"10.1109/STA.2014.7086708","DOIUrl":null,"url":null,"abstract":"In this work, a model based T-S fuzzy predictive control LMI optimization is introduced. The aim of discrete T-S fuzzy predictive controller is to drive the state of the system to the original state where a stabilizing controller is ensured. The stability of the controlled systems is studied using non quadratic case of the Lyapunov function and adopting of Non-PDC controller. The stability is guaranteed based on the conditions expressed of terms of LMIs. The optimal solution has been obtained at each sampling time. The results are shows the effectiveness of this strategy.","PeriodicalId":125957,"journal":{"name":"2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"253 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"New approach of model based T-S fuzzy predictive control using LMI approach\",\"authors\":\"A. Zahaf, B. Boutamina, S. Bououden, S. Filali\",\"doi\":\"10.1109/STA.2014.7086708\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, a model based T-S fuzzy predictive control LMI optimization is introduced. The aim of discrete T-S fuzzy predictive controller is to drive the state of the system to the original state where a stabilizing controller is ensured. The stability of the controlled systems is studied using non quadratic case of the Lyapunov function and adopting of Non-PDC controller. The stability is guaranteed based on the conditions expressed of terms of LMIs. The optimal solution has been obtained at each sampling time. The results are shows the effectiveness of this strategy.\",\"PeriodicalId\":125957,\"journal\":{\"name\":\"2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)\",\"volume\":\"253 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/STA.2014.7086708\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/STA.2014.7086708","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
New approach of model based T-S fuzzy predictive control using LMI approach
In this work, a model based T-S fuzzy predictive control LMI optimization is introduced. The aim of discrete T-S fuzzy predictive controller is to drive the state of the system to the original state where a stabilizing controller is ensured. The stability of the controlled systems is studied using non quadratic case of the Lyapunov function and adopting of Non-PDC controller. The stability is guaranteed based on the conditions expressed of terms of LMIs. The optimal solution has been obtained at each sampling time. The results are shows the effectiveness of this strategy.