{"title":"Genetic algorithms for tuning PLC loops","authors":"P. Dadone, H. Vanlandingham","doi":"10.1109/SMCIA.1999.782708","DOIUrl":null,"url":null,"abstract":"A common practice is to design a controller by plant observations (i.e. experiments) and to optimize some of its parameters by trial-and-error. This paper proposes a genetic algorithm for the automation of the search procedure and its implementation on a programmable logic controller (PLC). The details of this implementation are discussed, along with an example one carried out for the control of a plant simulation problem introduced by Eastman Chemical Co. The advantage of such an approach consists of automating the search for a good solution. Moreover, the genetic algorithm can be easily programmed in the PLC and reused for different plants with the only need for string encoding and fitness evaluation reprogramming.","PeriodicalId":222278,"journal":{"name":"SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SMCia/99 Proceedings of the 1999 IEEE Midnight - Sun Workshop on Soft Computing Methods in Industrial Applications (Cat. No.99EX269)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMCIA.1999.782708","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
A common practice is to design a controller by plant observations (i.e. experiments) and to optimize some of its parameters by trial-and-error. This paper proposes a genetic algorithm for the automation of the search procedure and its implementation on a programmable logic controller (PLC). The details of this implementation are discussed, along with an example one carried out for the control of a plant simulation problem introduced by Eastman Chemical Co. The advantage of such an approach consists of automating the search for a good solution. Moreover, the genetic algorithm can be easily programmed in the PLC and reused for different plants with the only need for string encoding and fitness evaluation reprogramming.