{"title":"Model Reduction of Linear Systems by Using Improved Mihailov Stability Criterion","authors":"A. Prajapati, R. Prasad","doi":"10.1109/CICN.2019.8902463","DOIUrl":null,"url":null,"abstract":"In this paper, a new model reduction method is proposed for the simplification of the complexity of large scale models. The proposed technique is based on the Mihailov stability criterion and it assured the stability of the reduced system, if the higher order model is stable. In this method, the denominator polynomial of the reduced model is determined by Mihailov stability criterion and the numerator polynomial is obtained by using a simple mathematical algorithm discussed in the literature. The superiority of the proposed technique is judged by comparing the time responses of original and reduced systems. The performance of the proposed method is measured in terms of various error indices such as integral square error (ISE), relative integral square error (RISE), integral absolute error (IAE) and integral time absolute error (ITAE). It is seen that the results of the standard numerical example are quite competitive as compared to the standard model order reduction methods.","PeriodicalId":329966,"journal":{"name":"2019 11th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"271 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 11th International Conference on Computational Intelligence and Communication Networks (CICN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CICN.2019.8902463","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
In this paper, a new model reduction method is proposed for the simplification of the complexity of large scale models. The proposed technique is based on the Mihailov stability criterion and it assured the stability of the reduced system, if the higher order model is stable. In this method, the denominator polynomial of the reduced model is determined by Mihailov stability criterion and the numerator polynomial is obtained by using a simple mathematical algorithm discussed in the literature. The superiority of the proposed technique is judged by comparing the time responses of original and reduced systems. The performance of the proposed method is measured in terms of various error indices such as integral square error (ISE), relative integral square error (RISE), integral absolute error (IAE) and integral time absolute error (ITAE). It is seen that the results of the standard numerical example are quite competitive as compared to the standard model order reduction methods.