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

José Luis Torres-Moreno1, José Luis Blanco-Claraco2, Antonio Giménez-Fernández3

  • 1Department 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. jltmoreno@ual.es.

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Summary

This study presents real-time online estimation methods for mechanism states using noisy sensor data. Novel Kalman filter integration handles constrained systems, validated experimentally for multibody dynamics.

Keywords:
Kalman filterdynamics of multibody systemsinertial measurement unitskinematicssimulationstate estimationtestbed

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Area of Science:

  • Multibody Dynamics and Control Systems
  • Robotics and Mechatronics
  • State Estimation and Sensor Fusion

Background:

  • Estimating kinematic and dynamic states of mechanisms from noisy measurements presents significant challenges.
  • Existing methods struggle with closed-loop, constrained mechanisms due to interdependent state variables.
  • Inertial Measurement Units (IMUs) offer a promising, albeit noisy, data source for state estimation.

Purpose of the Study:

  • To develop and validate novel online estimation techniques for kinematic and dynamic states of constrained mechanisms.
  • To adapt Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) for real-time state estimation in multibody systems.
  • To address limitations of traditional estimators when applied to systems with dependent coordinates.

Main Methods:

  • Derivation of mechanism states (position, velocity, acceleration) from IMU signals using multibody kinematics.
  • Integration of generic multibody dynamic equations into EKF and UKF variants.
  • Application of estimators on manifolds of allowed positions and velocities by estimating independent coordinates.

Main Results:

  • Successful online estimation of kinematic and dynamic states for a planar four-bar linkage using IMUs.
  • Experimental validation confirms the accuracy and real-time capability of the proposed EKF and UKF approaches.
  • The method effectively handles closed-loop, constrained mechanisms, overcoming limitations of previous techniques.

Conclusions:

  • The proposed Kalman filtering approach provides an effective solution for real-time online state estimation in complex multibody systems.
  • Estimating independent coordinates on constraint manifolds enables robust state tracking for mechanisms previously difficult to model.
  • This work advances the field of multibody dynamics by offering a practical and experimentally validated real-time estimation methodology.