E. Herrera-López, Bernardino Castillo, Jesús Ramírez, E. Ferreira
{"title":"Exact Fuzzy Observer for a Baker's Yeast Fed-Batch Fermentation Process","authors":"E. Herrera-López, Bernardino Castillo, Jesús Ramírez, E. Ferreira","doi":"10.1109/FUZZY.2007.4295502","DOIUrl":null,"url":null,"abstract":"The purpose of this work is to design an exact fuzzy observer for a bioprocess switching between two different metabolic states. A fed-batch baker's yeast culture is modeled by two sub-models: a respiro-fermentative state with ethanol production and a respirative state with ethanol consumption. An exact fuzzy observer model using sector nonlinearity was built for both nonlinear models; the observer gains were designed using Linear Matrix Inequalities (LMI's). The observer dynamics shows a very good tracking behavior with respect of the states of the switching partial models. The observer premise variables depend on the state variables estimated by the fuzzy observer.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"274 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Fuzzy Systems Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.2007.4295502","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
The purpose of this work is to design an exact fuzzy observer for a bioprocess switching between two different metabolic states. A fed-batch baker's yeast culture is modeled by two sub-models: a respiro-fermentative state with ethanol production and a respirative state with ethanol consumption. An exact fuzzy observer model using sector nonlinearity was built for both nonlinear models; the observer gains were designed using Linear Matrix Inequalities (LMI's). The observer dynamics shows a very good tracking behavior with respect of the states of the switching partial models. The observer premise variables depend on the state variables estimated by the fuzzy observer.