{"title":"Recursive Least Squares with Matrix Forgetting","authors":"Adam L. Bruce, A. Goel, D. Bernstein","doi":"10.23919/ACC45564.2020.9148005","DOIUrl":null,"url":null,"abstract":"This paper considers an extension of recursive least squares (RLS), where the cost function is modified to include a matrix forgetting factor. Minimization of the modified cost function provides a framework for combined variable-rate and variable-direction (RLS-VRDF) forgetting. This extension of RLS simultaneously addresses two key issues in standard RLS, namely, the need for variable-rate forgetting due to changing plant parameters as well as the need for variable-direction covariance updating due to the loss of persistency. The performance of RSL-VRDF is illustrated by an example with abrupt parameter changes and loss of persistency.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 American Control Conference (ACC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/ACC45564.2020.9148005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
This paper considers an extension of recursive least squares (RLS), where the cost function is modified to include a matrix forgetting factor. Minimization of the modified cost function provides a framework for combined variable-rate and variable-direction (RLS-VRDF) forgetting. This extension of RLS simultaneously addresses two key issues in standard RLS, namely, the need for variable-rate forgetting due to changing plant parameters as well as the need for variable-direction covariance updating due to the loss of persistency. The performance of RSL-VRDF is illustrated by an example with abrupt parameter changes and loss of persistency.