{"title":"A new bias-compensating least-squares method for continuous-time MIMO system identification applied to a laboratory-scale process","authors":"H. Garnier, P. Sibille, H. Nguyen","doi":"10.1109/CCA.1994.381459","DOIUrl":null,"url":null,"abstract":"This paper presents a new bias-compensating least-squares method for the identification of continuous-time transfer function matrix model of multi-input multi-output (MIMO) systems. The proposed method uses the generalised Poisson moment functional approach for handling time derivatives and is applied to the identification of a laboratory-scale process which simulates industrial material transport control problems. Model validation results show the potentiality of the proposed method in practical applications.<<ETX>>","PeriodicalId":173370,"journal":{"name":"1994 Proceedings of IEEE International Conference on Control and Applications","volume":"94 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"1994 Proceedings of IEEE International Conference on Control and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCA.1994.381459","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
This paper presents a new bias-compensating least-squares method for the identification of continuous-time transfer function matrix model of multi-input multi-output (MIMO) systems. The proposed method uses the generalised Poisson moment functional approach for handling time derivatives and is applied to the identification of a laboratory-scale process which simulates industrial material transport control problems. Model validation results show the potentiality of the proposed method in practical applications.<>
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