{"title":"Leverrier algorithm based reduced order modeling of dc-dc converters","authors":"M. Garg, Y. V. Hote","doi":"10.1109/IICPE.2014.7115838","DOIUrl":null,"url":null,"abstract":"In this paper, mathematical modeling of dc-dc buck and boost converter is presented using Leverrier algorithm. The main advantage of this method is that there is no need to determine the inverse of a matrix and hence, this method is computationally efficient. Further, it shown that by reducing second order model of dc-dc buck and boost converter to first order, similar performance can be achieved. The simulation results and performance evaluation are carried out using MATLAB, which depicts that original system responses match well with responses of reduced order models.","PeriodicalId":206767,"journal":{"name":"2014 IEEE 6th India International Conference on Power Electronics (IICPE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 6th India International Conference on Power Electronics (IICPE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IICPE.2014.7115838","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
In this paper, mathematical modeling of dc-dc buck and boost converter is presented using Leverrier algorithm. The main advantage of this method is that there is no need to determine the inverse of a matrix and hence, this method is computationally efficient. Further, it shown that by reducing second order model of dc-dc buck and boost converter to first order, similar performance can be achieved. The simulation results and performance evaluation are carried out using MATLAB, which depicts that original system responses match well with responses of reduced order models.