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An Enhanced Adaptive Kalman Filter for Multibody Model Observation.

Antonio J Rodríguez1, Emilio Sanjurjo1, Miguel Ángel Naya1

  • 1Laboratory of Mechanical Engineering, CITENI, Campus Industrial de Ferrol, Universidade da Coruña, 15403 Ferrol, Spain.

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Summary

This study introduces a new adaptive method for tuning Kalman filter parameters in multibody systems. The approach enhances state estimation accuracy for virtual sensors and digital twins.

Keywords:
Kalman filteradaptive Kalman filtercolored noisedigital twinforce estimationmultibody dynamicsnoise modelingshaping filtervirtual sensing

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Area of Science:

  • Engineering
  • Control Systems
  • Computational Mechanics

Background:

  • State estimation using Kalman filters and multibody models is crucial for virtual sensors and digital twins.
  • A key challenge is tuning filter parameters, especially the plant noise covariance matrix.

Purpose of the Study:

  • To present a novel method for adaptive tuning of plant noise parameters in Kalman filters for multibody systems.
  • To improve the accuracy and robustness of state estimation compared to existing methods.

Main Methods:

  • Developed a new method incorporating a shaping filter to whiten plant noise.
  • Implemented an adaptive algorithm to automatically adjust plant noise parameters.
  • Validated the method using the 'three-simulation method' and compared it with literature approaches.

Main Results:

  • The new adaptive method demonstrates accuracy comparable to or exceeding the best hand-tuned filters.
  • It significantly improves upon previously published adaptive state estimation techniques.
  • The developed methods are available in the open-source MBDE library (MATLAB).

Conclusions:

  • The proposed adaptive Kalman filter tuning method offers enhanced state estimation for multibody systems.
  • This advancement is valuable for control applications and digital twin technology.
  • The open-source MBDE library facilitates the adoption of these advanced techniques.