Constrained dynamic parameter estimation using the Extended Kalman Filter

Vladimir Joukov, Vincent Bonnet, Gentiane Venture, Dana Kulic

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)

Abstract

In this paper we present a real-time method for identification of the dynamic parameters of a manipulator and its load using kinematic measurements and either joint torques or force and moment at the base. The parameters are estimated using the Extended Kalman Filter and constraints are imposed using Sigmoid functions to ensure the parameters remain within their physically feasible ranges, such as links having positive masses and moments of inertia. Identified parameters can be used in model based controllers. The presented approach is validated through simulation and on data collected with the Barret WAM manipulator. Using the estimated parameters instead of ones provided by the manufacturer greatly improves joint torque prediction.

Original languageEnglish
Title of host publication2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015)
EditorsAlois Knoll
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages3654-3659
Number of pages6
ISBN (Electronic)9781479999941, 9781479999934
ISBN (Print)9781479999958
DOIs
Publication statusPublished - 17 Dec 2015
Externally publishedYes
EventIEEE/RSJ International Conference on Intelligent Robots and Systems 2015 - Hamburg, Germany
Duration: 28 Sep 20152 Oct 2015
https://ieeexplore.ieee.org/xpl/conhome/7347169/proceeding (Proceedings)

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems 2015
Abbreviated titleIROS 2015
CountryGermany
CityHamburg
Period28/09/152/10/15
Internet address

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