{"title":"Fuzzy linearization for nonlinear systems: a preliminary study","authors":"Jin Yaochu, Zhu Jing, Jian Jingping","doi":"10.1109/FUZZY.1994.343601","DOIUrl":null,"url":null,"abstract":"This paper proposes a novel control diagram for nonlinear systems, namely fuzzy linearization. On the basis of fuzzy reasoning, we build a set of fuzzy linear subsystems to linearize the original nonlinear system. Consequently, we design an optimal controller for every linear subsystem using the mature linear control theory. The control effect of each subsystem is composed via fuzzy reasoning to control the nonlinear system. Therefore, the design of any nonlinear systems can be simplified to the control problem of linear time-invariant systems. Compared to the existing methods such as the Taylor expansion and piecewise linearization, the proposed approach exhibits higher precision, better control performances and stronger robustness to system uncertainties.<<ETX>>","PeriodicalId":153967,"journal":{"name":"Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference","volume":"69 4","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.1994.343601","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
This paper proposes a novel control diagram for nonlinear systems, namely fuzzy linearization. On the basis of fuzzy reasoning, we build a set of fuzzy linear subsystems to linearize the original nonlinear system. Consequently, we design an optimal controller for every linear subsystem using the mature linear control theory. The control effect of each subsystem is composed via fuzzy reasoning to control the nonlinear system. Therefore, the design of any nonlinear systems can be simplified to the control problem of linear time-invariant systems. Compared to the existing methods such as the Taylor expansion and piecewise linearization, the proposed approach exhibits higher precision, better control performances and stronger robustness to system uncertainties.<>
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