{"title":"Stochastic fuzzy servo control using multiple linear dynamic models","authors":"Keigo Watanabe, K. Izumi, Fuha Han","doi":"10.1109/KES.1998.726011","DOIUrl":null,"url":null,"abstract":"A fuzzy servo system is described for a system with noises by using a stochastic fuzzy control method with some linear dynamic models. The fuzzy servo approach is applied to the control of rotational angle for an omnidirectional mobile robot with three orthogonal-wheel assemblies. The stochastic fuzzy servo method and its modified method with a static evaluation on the model confidence are implemented for some simulations of the mobile robot. In particular, two improvement methods are shown for the case when a set of the prespecified linear models does not include a model comparable to the reference rotational angle.","PeriodicalId":394492,"journal":{"name":"1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KES.1998.726011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
A fuzzy servo system is described for a system with noises by using a stochastic fuzzy control method with some linear dynamic models. The fuzzy servo approach is applied to the control of rotational angle for an omnidirectional mobile robot with three orthogonal-wheel assemblies. The stochastic fuzzy servo method and its modified method with a static evaluation on the model confidence are implemented for some simulations of the mobile robot. In particular, two improvement methods are shown for the case when a set of the prespecified linear models does not include a model comparable to the reference rotational angle.
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