{"title":"An approach for closed loop system identification","authors":"I. Dore Landau, F. Rolland","doi":"10.1109/CDC.1994.411602","DOIUrl":null,"url":null,"abstract":"In this paper one investigates an approach for system identification in closed loop starting from the pole placement control design using an R-S-T controller. A convenient re-parametrization of the prefilter (T) leads to error equations which are linear in the plant parameter errors and with a regressor which depends exclusively upon the external reference signals. This allows to derive parameter estimation algorithms which are asymptotically unbiased in the presence of noise. A convergence analysis of the parameter estimation algorithm in a stochastic environment using the ODE approach is presented. Simulations illustrate the potentiality of the proposed approach.<<ETX>>","PeriodicalId":355623,"journal":{"name":"Proceedings of 1994 33rd IEEE Conference on Decision and Control","volume":"74 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1994 33rd IEEE Conference on Decision and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.1994.411602","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
In this paper one investigates an approach for system identification in closed loop starting from the pole placement control design using an R-S-T controller. A convenient re-parametrization of the prefilter (T) leads to error equations which are linear in the plant parameter errors and with a regressor which depends exclusively upon the external reference signals. This allows to derive parameter estimation algorithms which are asymptotically unbiased in the presence of noise. A convergence analysis of the parameter estimation algorithm in a stochastic environment using the ODE approach is presented. Simulations illustrate the potentiality of the proposed approach.<>