{"title":"Parameter Identification in Mechanical Systems with Energy-Based Regressor: Preliminary Study","authors":"A. Sabirova, Simeon Nedelchev, I. Gaponov","doi":"10.1109/NIR52917.2021.9665801","DOIUrl":null,"url":null,"abstract":"Parameter estimation in physical systems is an important area of modern engineering and robotics. This paper discusses parameter identification in linear, time-invariant mechanical systems based on their energy. The described approach employs system’s regressor derived from the energy equation with subsequent least-squares estimation. This method has been evaluated experimentally on a single degree-of-freedom pendulum and demonstrated high accuracy and quick parameter convergence, while not suffering from the drawbacks of conventional dynamics-based identification.","PeriodicalId":333109,"journal":{"name":"2021 International Conference \"Nonlinearity, Information and Robotics\" (NIR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference \"Nonlinearity, Information and Robotics\" (NIR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NIR52917.2021.9665801","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Parameter estimation in physical systems is an important area of modern engineering and robotics. This paper discusses parameter identification in linear, time-invariant mechanical systems based on their energy. The described approach employs system’s regressor derived from the energy equation with subsequent least-squares estimation. This method has been evaluated experimentally on a single degree-of-freedom pendulum and demonstrated high accuracy and quick parameter convergence, while not suffering from the drawbacks of conventional dynamics-based identification.