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A separable prediction error method for robot identification

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-01490600
Contributor : Open Archive Toulouse Archive Ouverte (oatao) <>
Submitted on : Wednesday, March 22, 2017 - 3:37:19 PM
Last modification on : Tuesday, September 21, 2021 - 4:12:13 PM
Long-term archiving on: : Friday, June 23, 2017 - 12:17:20 PM

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Mathieu Brunot, Alexandre Janot, Francisco Carrillo, Maxime Gautier. A separable prediction error method for robot identification. 7th IFAC Symposium on Mechatronic Systems (MECHATRONICS 2016), Sep 2016, Loughborough, United Kingdom. pp.487-492, ⟨10.1016/j.ifacol.2016.10.650⟩. ⟨hal-01490600⟩

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