{"title":"Time-Delay System Identification Using Genetic Algorithm - Part Two: FOPDT/SOPDT Model Approximation","authors":"Zhenyu Yang, G. T. Seested","doi":"10.3182/20130902-3-CN-3020.00117","DOIUrl":null,"url":null,"abstract":"Abstract The First-Order-Plus-Dead-Time (FOPDT) or Second-Order-Plus-Dead-Time (SOPDT) model approximation to a complicated process system can be carried out through either a kind of model reduction approach or a kind of system identification approach. This paper investigates this model approximation problem through an identification approach using the real coded Genetic Algorithm (GA). The desired FOPDT/SOPDT model is directly identified based on the measured system's input and output data. In order to evaluate the quality and performance of this GA-based approach, the proposed method is compared with two typical model reduction methods, namely Skogestad's rules and Sung et al method. The obtained results exhibit a very promising capability of GA in handling the data-driven time-delay system approximation.","PeriodicalId":90521,"journal":{"name":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3182/20130902-3-CN-3020.00117","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
Abstract The First-Order-Plus-Dead-Time (FOPDT) or Second-Order-Plus-Dead-Time (SOPDT) model approximation to a complicated process system can be carried out through either a kind of model reduction approach or a kind of system identification approach. This paper investigates this model approximation problem through an identification approach using the real coded Genetic Algorithm (GA). The desired FOPDT/SOPDT model is directly identified based on the measured system's input and output data. In order to evaluate the quality and performance of this GA-based approach, the proposed method is compared with two typical model reduction methods, namely Skogestad's rules and Sung et al method. The obtained results exhibit a very promising capability of GA in handling the data-driven time-delay system approximation.