{"title":"Robust fuzzy PID controller design for dynamic systems with time delay","authors":"Fernanda Lima, G. Serra","doi":"10.1109/ISIE.2015.7281454","DOIUrl":null,"url":null,"abstract":"A robust fuzzy control design based on gain and phase margins specifications for nonlinear systems, with time delay, via multiobjective genetic algorithm, in the continuous time domain, is proposed. A Fuzzy C-Means (FCM) clustering algorithm estimates the antecedent parameters and the rule number of a Takagi-Sugeno fuzzy model, through from input and output data of the process, whereas the least squares algorithm estimates the consequent parameters. A multiobjective genetic strategy is defined to tune the fuzzy controller parameters, so the gain and phase margins of the fuzzy control system are close to the specified ones. The fuzzy PID controller was implemented on a real time acquisition data platform and compared with the PID controller Ziegler Nichols. The results demonstrate the effectiveness and practical viability of the proposed methodology.","PeriodicalId":377110,"journal":{"name":"2015 IEEE 24th International Symposium on Industrial Electronics (ISIE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 24th International Symposium on Industrial Electronics (ISIE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIE.2015.7281454","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
A robust fuzzy control design based on gain and phase margins specifications for nonlinear systems, with time delay, via multiobjective genetic algorithm, in the continuous time domain, is proposed. A Fuzzy C-Means (FCM) clustering algorithm estimates the antecedent parameters and the rule number of a Takagi-Sugeno fuzzy model, through from input and output data of the process, whereas the least squares algorithm estimates the consequent parameters. A multiobjective genetic strategy is defined to tune the fuzzy controller parameters, so the gain and phase margins of the fuzzy control system are close to the specified ones. The fuzzy PID controller was implemented on a real time acquisition data platform and compared with the PID controller Ziegler Nichols. The results demonstrate the effectiveness and practical viability of the proposed methodology.