{"title":"Temperature Control Using Intelligent Techniques","authors":"S. Saini, S. Rani","doi":"10.1109/ACCT.2012.110","DOIUrl":null,"url":null,"abstract":"This paper presents the comparison of various intelligent techniques used for temperature control of water bath system. Different control schemes namely PID, PID using Genetic Algorithms(GA-PID), Fuzzy Logic Control, Neural Network, Adaptive Neuro-Fuzzy Inference System(ANFIS), and GA-ANFIS have been compared through experimental studies with respect to set-points regulation, ramp-points tracking, influence of unknown impulse noise and large parameter variation. The merits and limitations of each scheme has been brought out. It has been found that the use of advanced techniques such as Artificial Neural Networks (ANN) and GA with the conventional FLC offers encouraging advantages. The superiority of FLC over PID, ANFIS over FLC and GA-ANFIS over other schemes has been highlighted.","PeriodicalId":396313,"journal":{"name":"2012 Second International Conference on Advanced Computing & Communication Technologies","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Second International Conference on Advanced Computing & Communication Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACCT.2012.110","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17
Abstract
This paper presents the comparison of various intelligent techniques used for temperature control of water bath system. Different control schemes namely PID, PID using Genetic Algorithms(GA-PID), Fuzzy Logic Control, Neural Network, Adaptive Neuro-Fuzzy Inference System(ANFIS), and GA-ANFIS have been compared through experimental studies with respect to set-points regulation, ramp-points tracking, influence of unknown impulse noise and large parameter variation. The merits and limitations of each scheme has been brought out. It has been found that the use of advanced techniques such as Artificial Neural Networks (ANN) and GA with the conventional FLC offers encouraging advantages. The superiority of FLC over PID, ANFIS over FLC and GA-ANFIS over other schemes has been highlighted.