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Sensors 2016, 16(3), 333; doi:10.3390/s16030333

Online Kinematic and Dynamic-State Estimation for Constrained Multibody Systems Based on IMUs

1
Department of Engineering, Automatic Control, Robotics and Mechatronics Research Group, University of Almería, Agrifood Campus of International Excellence (ceiA3), CIESOL, Joint Center University of Almería-CIEMAT, Almería 04120, Spain
2
Mechanical Engineering Laboratory, University of A Coruña, Escuela Politécnica Superior, Mendizábal s/n, Ferrol 15403, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Gert F. Trommer
Received: 31 October 2015 / Revised: 24 February 2016 / Accepted: 2 March 2016 / Published: 4 March 2016
(This article belongs to the Section Physical Sensors)
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Abstract

This article addresses the problems of online estimations of kinematic and dynamic states of a mechanism from a sequence of noisy measurements. In particular, we focus on a planar four-bar linkage equipped with inertial measurement units (IMUs). Firstly, we describe how the position, velocity, and acceleration of all parts of the mechanism can be derived from IMU signals by means of multibody kinematics. Next, we propose the novel idea of integrating the generic multibody dynamic equations into two variants of Kalman filtering, i.e., the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), in a way that enables us to handle closed-loop, constrained mechanisms, whose state space variables are not independent and would normally prevent the direct use of such estimators. The proposal in this work is to apply those estimators over the manifolds of allowed positions and velocities, by means of estimating a subset of independent coordinates only. The proposed techniques are experimentally validated on a testbed equipped with encoders as a means of establishing the ground-truth. Estimators are run online in real-time, a feature not matched by any previous procedure of those reported in the literature on multibody dynamics. View Full-Text
Keywords: kinematics; dynamics of multibody systems; simulation; state estimation; Kalman filter; testbed; inertial measurement units kinematics; dynamics of multibody systems; simulation; state estimation; Kalman filter; testbed; inertial measurement units
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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. (CC BY 4.0).

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MDPI and ACS Style

Torres-Moreno, J.L.; Blanco-Claraco, J.L.; Giménez-Fernández, A.; Sanjurjo, E.; Naya, M.Á. Online Kinematic and Dynamic-State Estimation for Constrained Multibody Systems Based on IMUs. Sensors 2016, 16, 333.

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