I. K. Bousserhane, A. Hazzab, M. Rahli, M. Kamli, B. Mazari
{"title":"Adaptive PI Controller using Fuzzy System Optimized by Genetic Algorithm for Induction Motor Control","authors":"I. K. Bousserhane, A. Hazzab, M. Rahli, M. Kamli, B. Mazari","doi":"10.1109/CIEP.2006.312162","DOIUrl":null,"url":null,"abstract":"In this paper, an optimal fuzzy gain scheduling of PI controller is adopted to speed control of an induction motor. First, a designed fuzzy gain scheduling of PI controller is investigated, in which fuzzy rules are utilized on-line to adapt the PI controller parameters based on the error and its first time derivative. However, the major disadvantage of the fuzzy logic control is the lack of design techniques, for this purpose we propose an optimization technique of the fuzzy logic adapter parameters using genetic algorithm. The effectiveness of the complete proposed control scheme is verified by numerical simulation. The numerical validation results of the proposed scheme have presented good performances compared to the fuzzy controller which have parameters chosen by the human operator","PeriodicalId":131301,"journal":{"name":"2006 IEEE International Power Electronics Congress","volume":"128 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE International Power Electronics Congress","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIEP.2006.312162","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 25
Abstract
In this paper, an optimal fuzzy gain scheduling of PI controller is adopted to speed control of an induction motor. First, a designed fuzzy gain scheduling of PI controller is investigated, in which fuzzy rules are utilized on-line to adapt the PI controller parameters based on the error and its first time derivative. However, the major disadvantage of the fuzzy logic control is the lack of design techniques, for this purpose we propose an optimization technique of the fuzzy logic adapter parameters using genetic algorithm. The effectiveness of the complete proposed control scheme is verified by numerical simulation. The numerical validation results of the proposed scheme have presented good performances compared to the fuzzy controller which have parameters chosen by the human operator