A Separable Prediction Error 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 ; Predictions error methods ; Output error identification
• 刊名：IFAC-PapersOnLine
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：49
• 期：21
• 页码：487-492
• 全文大小：681 K

文摘

The Prediction Error Method, developed in the field of system identification, handles the identification of discrete time noise model for systems linear with respect to the states and the parameters. However, robots are represented by continuous time models, which are not linear with respect to the states. In this article, we consider the issue of robot identification, taking into account the physical parameters as well as the noise model in order to improve the accuracy of the estimates. Thus, we developed a new technique to tackle this problem. The experimental results tend to show a real improvement in the estimation accuracy.