Design and Implementation of Fuzzy Supervisory Controllers Using Fuzzy c-Means Clustering Combined with Fuzzy Gain Scheduling for a Binary Distillation Column
{"title":"Design and Implementation of Fuzzy Supervisory Controllers Using Fuzzy c-Means Clustering Combined with Fuzzy Gain Scheduling for a Binary Distillation Column","authors":"K. Somsung, S. Pratishthananda","doi":"10.1109/ICCIS.2006.252355","DOIUrl":null,"url":null,"abstract":"In this paper, fuzzy supervisory PI controllers are developed and implemented on a pilot plant binary distillation column. Fuzzy c-means clustering technique is used in selecting membership functions and fuzzy rules are determined using fuzzy gain scheduling technique. Thus, the need of heuristic method for designing fuzzy membership functions and rules from expert knowledge is omitted. Then, the fuzzy supervisors adapt the parameters of the PI controllers on line. The task of the controllers is to perform dual composition control of the top and bottom products when the disturbances enter the column in the form of changes in feed flow rate. The results show that the fuzzy supervisory PI controllers achieve much better performance than the fixed PI controllers","PeriodicalId":296028,"journal":{"name":"2006 IEEE Conference on Cybernetics and Intelligent Systems","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE Conference on Cybernetics and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIS.2006.252355","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
In this paper, fuzzy supervisory PI controllers are developed and implemented on a pilot plant binary distillation column. Fuzzy c-means clustering technique is used in selecting membership functions and fuzzy rules are determined using fuzzy gain scheduling technique. Thus, the need of heuristic method for designing fuzzy membership functions and rules from expert knowledge is omitted. Then, the fuzzy supervisors adapt the parameters of the PI controllers on line. The task of the controllers is to perform dual composition control of the top and bottom products when the disturbances enter the column in the form of changes in feed flow rate. The results show that the fuzzy supervisory PI controllers achieve much better performance than the fixed PI controllers