{"title":"Temperature control of green house system using evolutionary computation","authors":"R. Umashankari, K. Valarmathi, G. SaravanaKumar","doi":"10.1109/ICEETS.2013.6533490","DOIUrl":null,"url":null,"abstract":"This paper deals with the controlling problem of the inside temperature of the greenhouse. The control objective is to tune the control parameters for the system using evolutionary computation and to minimize the error. In this paper, the Maximal Stability Degree (MSD) based approach is applied to obtain the control parameters. When the control parameters are identified, then Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are applied to optimize the controller parameter. The simulation results show that the proposed PSO technique is effective in identifying the parameters and has resulted in a minimum value of overshoot, rise time, peak value and settling time as compared to other methods.","PeriodicalId":319201,"journal":{"name":"2013 International Conference on Energy Efficient Technologies for Sustainability","volume":"31 4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Energy Efficient Technologies for Sustainability","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEETS.2013.6533490","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
This paper deals with the controlling problem of the inside temperature of the greenhouse. The control objective is to tune the control parameters for the system using evolutionary computation and to minimize the error. In this paper, the Maximal Stability Degree (MSD) based approach is applied to obtain the control parameters. When the control parameters are identified, then Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are applied to optimize the controller parameter. The simulation results show that the proposed PSO technique is effective in identifying the parameters and has resulted in a minimum value of overshoot, rise time, peak value and settling time as compared to other methods.