M. Kakanov, S. Tomashevich, V. Gromov, O. Borisov, F. B. Gromova, A. Pyrkin
{"title":"四旋翼模型的参数估计","authors":"M. Kakanov, S. Tomashevich, V. Gromov, O. Borisov, F. B. Gromova, A. Pyrkin","doi":"10.1109/NIR50484.2020.9290199","DOIUrl":null,"url":null,"abstract":"This article is devoted to the estimation of unknown parameters of the quadrotor dynamic model. The linear regression model of the quadrotor was obtained. To estimate the unknown parameters of the regression model, the gradient approach and its modifications, such as the advanced Kalman filter, and dynamic regressor extension and mixing (DREM) methods were applied. The effectiveness of the proposed methods was confirmed by simulation. The results showed the benefits of the DREM algorithm, in particular, concerning the convergence rate.","PeriodicalId":274976,"journal":{"name":"2020 International Conference Nonlinearity, Information and Robotics (NIR)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Parameter Estimation of Quadrotor Model\",\"authors\":\"M. Kakanov, S. Tomashevich, V. Gromov, O. Borisov, F. B. Gromova, A. Pyrkin\",\"doi\":\"10.1109/NIR50484.2020.9290199\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article is devoted to the estimation of unknown parameters of the quadrotor dynamic model. The linear regression model of the quadrotor was obtained. To estimate the unknown parameters of the regression model, the gradient approach and its modifications, such as the advanced Kalman filter, and dynamic regressor extension and mixing (DREM) methods were applied. The effectiveness of the proposed methods was confirmed by simulation. The results showed the benefits of the DREM algorithm, in particular, concerning the convergence rate.\",\"PeriodicalId\":274976,\"journal\":{\"name\":\"2020 International Conference Nonlinearity, Information and Robotics (NIR)\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference Nonlinearity, Information and Robotics (NIR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NIR50484.2020.9290199\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference Nonlinearity, Information and Robotics (NIR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NIR50484.2020.9290199","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This article is devoted to the estimation of unknown parameters of the quadrotor dynamic model. The linear regression model of the quadrotor was obtained. To estimate the unknown parameters of the regression model, the gradient approach and its modifications, such as the advanced Kalman filter, and dynamic regressor extension and mixing (DREM) methods were applied. The effectiveness of the proposed methods was confirmed by simulation. The results showed the benefits of the DREM algorithm, in particular, concerning the convergence rate.
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