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.
Sensors (Basel, Switzerland)
|April 12, 2025
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.
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.
Keywords:
Kalman filteradaptive Kalman filtercolored noisedigital twinforce estimationmultibody dynamicsnoise modelingshaping filtervirtual sensingMore Related Videos
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