{"title":"Polynomial fuzzy modeling and tracking control of wheeled mobile robots via sum of squares approach","authors":"Cheol-Joong Kim, Ji-Wook Kwon, Dongkyoung Chwa","doi":"10.1109/ICIT.2009.4939656","DOIUrl":null,"url":null,"abstract":"This paper proposes the polynomial fuzzy modeling and tracking control methods for wheeled mobile robots by using sum of squares (SOS) approach, which is developed as SOSTOOL under the Matlab environment. Due to the polynomial fuzzy modeling, we can obtain the linearized tracking error dynamics such that both LMI (Linear Matrix Inequality) and SOS approaches can be applied. Since SOS approach handles more nonlinear system characteristics than LMI, we can obtain the better tracking performance, which is demonstrated in the numerical simulations. The proposed method has advantages in that the control structure can be simplified and it can be further extended to accommodate the input saturation, disturbance compensation, etc., which is well developed for LMI control methods.","PeriodicalId":405687,"journal":{"name":"2009 IEEE International Conference on Industrial Technology","volume":"126 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Industrial Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIT.2009.4939656","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
This paper proposes the polynomial fuzzy modeling and tracking control methods for wheeled mobile robots by using sum of squares (SOS) approach, which is developed as SOSTOOL under the Matlab environment. Due to the polynomial fuzzy modeling, we can obtain the linearized tracking error dynamics such that both LMI (Linear Matrix Inequality) and SOS approaches can be applied. Since SOS approach handles more nonlinear system characteristics than LMI, we can obtain the better tracking performance, which is demonstrated in the numerical simulations. The proposed method has advantages in that the control structure can be simplified and it can be further extended to accommodate the input saturation, disturbance compensation, etc., which is well developed for LMI control methods.