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  • 标题:State Space Estimation Method for Robot Identification
  • 本地全文:下载
  • 作者:Mathieu Brunot ; Mathieu Brunot ; Alexandre Janot
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2016
  • 卷号:49
  • 期号:21
  • 页码:228-233
  • DOI:10.1016/j.ifacol.2016.10.555
  • 语种:English
  • 出版社:Elsevier
  • 摘要:Abstract: In this paper, we study the identification of robot dynamic models. The usual technique, based on the Least-Squares method, is carefully detailed. A new procedure based on Kalman filtering and fixed interval smoothing is developed. This new technique is compared to usual one with simulated and experimental data. The obtained results show that the proposed technique is a credible alternative, especially if the system bandwidth is unknown.
  • 关键词:KeywordsRobots identificationSystem identificationClosed-loop identificationLeast-squares identificationParameter identificationKalman filtersState observers
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