State Space Estimation Method for Robot Identification

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• 作者：Mathieu Brunot^* ; ^{Mathieu.Brunot@onera.fr" class="auth_mail" title="E-mail the corresponding author} ; Alexandre Janot^** ; ^{Alexandre.Janot@onera.fr" class="auth_mail" title="E-mail the corresponding author} ; Francisco Carrillo^*** ; ^{Francisco.Carrillo@enit.fr" class="auth_mail" title="E-mail the corresponding author} ; Maxime Gautier^**** ; ^{Maxime.Gautier@irccyn.ec-nantes.fr" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Robots identification ; System identification ; Closed-loop identification ; Least-squares identification ; Parameter identification ; Kalman filters ; State observers
• 刊名：IFAC-PapersOnLine
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：49
• 期：21
• 页码：228-233
• 全文大小：713 K

文摘

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.