{"title":"H∞ performance based filtering for time-varying systems via T-S fuzzy modelling","authors":"Fengqin Xia, Xiaojie Su, Rongni Yang","doi":"10.1109/ICARCV.2016.7838762","DOIUrl":null,"url":null,"abstract":"This paper considers H∞ reduced-order filtering problem for discrete-time Takagi-Sugeno (T-S) fuzzy systems with time-varying delay in its state. Firstly, Based on the reciprocally convex methods and a novel fuzzy Lyapunov functional, the proposed basis-dependent condition is utilized to ensure that the resulted error system is asymptotically stable with a prescribed H∞ performance and reduce the conservativeness. Then, By utilization of the convex linearization technique, the sufficient condition of reduced-order filter design can be casted into linear matrix inequality constraints. Finally, the desired filters can be obtained based on standard numerical algorithms.","PeriodicalId":128828,"journal":{"name":"2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARCV.2016.7838762","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper considers H∞ reduced-order filtering problem for discrete-time Takagi-Sugeno (T-S) fuzzy systems with time-varying delay in its state. Firstly, Based on the reciprocally convex methods and a novel fuzzy Lyapunov functional, the proposed basis-dependent condition is utilized to ensure that the resulted error system is asymptotically stable with a prescribed H∞ performance and reduce the conservativeness. Then, By utilization of the convex linearization technique, the sufficient condition of reduced-order filter design can be casted into linear matrix inequality constraints. Finally, the desired filters can be obtained based on standard numerical algorithms.