{"title":"Intelligent adaptive fuzzy control","authors":"Z. Dideková, S. Kajan, A. Kozáková, S. Kozák","doi":"10.1109/CYBERI.2018.8337549","DOIUrl":null,"url":null,"abstract":"The paper deals with the development of a new adaptive fuzzy control method and algorithm for nonlinear dynamic systems based on the hybrid approach using fuzzy logic and genetic techniques. The new hybrid control methodology based on adaptive switching uses the principle of control parameters adaptation for all operating points of a highly nonlinear process. The control algorithm is realized by a fuzzy controller with parameter optimization for different operating points using a genetic algorithm. Proposed theoretical results are verified on a case study dealing with control design for a nonlinear model of continuously stirred tank reactor. Obtained practical results confirm the high performance and possibility of implementation of this methodology for a broad real plants in industry.","PeriodicalId":6534,"journal":{"name":"2018 Cybernetics & Informatics (K&I)","volume":"74 1","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Cybernetics & Informatics (K&I)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CYBERI.2018.8337549","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
The paper deals with the development of a new adaptive fuzzy control method and algorithm for nonlinear dynamic systems based on the hybrid approach using fuzzy logic and genetic techniques. The new hybrid control methodology based on adaptive switching uses the principle of control parameters adaptation for all operating points of a highly nonlinear process. The control algorithm is realized by a fuzzy controller with parameter optimization for different operating points using a genetic algorithm. Proposed theoretical results are verified on a case study dealing with control design for a nonlinear model of continuously stirred tank reactor. Obtained practical results confirm the high performance and possibility of implementation of this methodology for a broad real plants in industry.