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Discrete Dynamics in Nature and Society
Volume 2010 (2010), Article ID 482972, 14 pages
Research Article

Extended and Unscented Kalman Filtering Applied to a Flexible-Joint Robot with Jerk Estimation

1Control Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166614776, Iran
2Department of Mechanical and Industrial Engineering, Concordia University, Montreal, QC, Canada H3G 1M8

Received 26 July 2010; Accepted 8 November 2010

Academic Editor: Recai Kilic

Copyright © 2010 Mohammad Ali Badamchizadeh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Robust nonlinear control of flexible-joint robots requires that the link position, velocity, acceleration, and jerk be available. In this paper, we derive the dynamic model of a nonlinear flexible-joint robot based on the governing Euler-Lagrange equations and propose extended and unscented Kalman filters to estimate the link acceleration and jerk from position and velocity measurements. Both observers are designed for the same model and run with the same covariance matrices under the same initial conditions. A five-bar linkage robot with revolute flexible joints is considered as a case study. Simulation results verify the effectiveness of the proposed filters.