{"title":"Intelligent predictive control of a power plant with evolutionary programming optimizer and neuro-fuzzy identifier","authors":"H. Ghezelayagh, Kwang Y. Lee","doi":"10.1109/CEC.2002.1004432","DOIUrl":null,"url":null,"abstract":"An intelligent predictive controller is implemented to control a fossil fuel power unit. This controller is a non-model based system that uses a self-organized neuro-fuzzy identifier to predict the response of the plant in a future time interval. The control inputs are optimized in this prediction horizon by evolutionary programming (EP) to minimize the error of identifier outputs and reference set points. The identifier performs automatic rule generation and membership function tuning by genetic algorithm (GA) and error back-propagation methods, respectively. This intelligent system provides a predictive control of multi-input multi-output nonlinear systems with slow time variation.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2002.1004432","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 22
Abstract
An intelligent predictive controller is implemented to control a fossil fuel power unit. This controller is a non-model based system that uses a self-organized neuro-fuzzy identifier to predict the response of the plant in a future time interval. The control inputs are optimized in this prediction horizon by evolutionary programming (EP) to minimize the error of identifier outputs and reference set points. The identifier performs automatic rule generation and membership function tuning by genetic algorithm (GA) and error back-propagation methods, respectively. This intelligent system provides a predictive control of multi-input multi-output nonlinear systems with slow time variation.
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