{"title":"具有不可测前提变量和干扰的Takagi-Sugeno模糊系统的自适应动态输出反馈控制","authors":"Balaje T. Thumati, A. Salour","doi":"10.1109/CICA.2014.7013231","DOIUrl":null,"url":null,"abstract":"Unlike in the literature, premise variables of the Takagi-Sugeno (TS) fuzzy system is assumed to be not measurable, and an adaptive output feedback control law is designed for the given system. Additionally, the system under investigation is considered to be subjected with both parameteric uncertainty and disturbance. Unlike other control designs, the bound on parameter uncertainty term is relaxed. Further, the adaptive control law utilizes estimated premise variables and online approximator. Note only one approximator is used to estimate both the parameter uncertainty and disturbance. Therefore, the proposed control design is simplified. This control design is guaranteed to render a stable closed loop TS fuzzy system. Detailed analytical results using Lyapunov theory are presented to guarantee stability. Finally, a simulation example is used to illustrate the performance of the proposed adaptive output feedback control law.","PeriodicalId":340740,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive dynamic output feedback control of Takagi-Sugeno fuzzy systems with immeasurable premise variables and disturbance\",\"authors\":\"Balaje T. Thumati, A. Salour\",\"doi\":\"10.1109/CICA.2014.7013231\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Unlike in the literature, premise variables of the Takagi-Sugeno (TS) fuzzy system is assumed to be not measurable, and an adaptive output feedback control law is designed for the given system. Additionally, the system under investigation is considered to be subjected with both parameteric uncertainty and disturbance. Unlike other control designs, the bound on parameter uncertainty term is relaxed. Further, the adaptive control law utilizes estimated premise variables and online approximator. Note only one approximator is used to estimate both the parameter uncertainty and disturbance. Therefore, the proposed control design is simplified. This control design is guaranteed to render a stable closed loop TS fuzzy system. Detailed analytical results using Lyapunov theory are presented to guarantee stability. Finally, a simulation example is used to illustrate the performance of the proposed adaptive output feedback control law.\",\"PeriodicalId\":340740,\"journal\":{\"name\":\"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CICA.2014.7013231\",\"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 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CICA.2014.7013231","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive dynamic output feedback control of Takagi-Sugeno fuzzy systems with immeasurable premise variables and disturbance
Unlike in the literature, premise variables of the Takagi-Sugeno (TS) fuzzy system is assumed to be not measurable, and an adaptive output feedback control law is designed for the given system. Additionally, the system under investigation is considered to be subjected with both parameteric uncertainty and disturbance. Unlike other control designs, the bound on parameter uncertainty term is relaxed. Further, the adaptive control law utilizes estimated premise variables and online approximator. Note only one approximator is used to estimate both the parameter uncertainty and disturbance. Therefore, the proposed control design is simplified. This control design is guaranteed to render a stable closed loop TS fuzzy system. Detailed analytical results using Lyapunov theory are presented to guarantee stability. Finally, a simulation example is used to illustrate the performance of the proposed adaptive output feedback control law.