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Sensor Selection and State Estimation for Unobservable and Non-Linear System Models.

Thijs Devos1,2, Matteo Kirchner1,2, Jan Croes1,2

  • 1LMSD Research Group, Department of Mechanical Engineering, KU Leuven, Celestijnenlaan 300, 3001 Leuven, Belgium.

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Summary
This summary is machine-generated.

This study introduces a new method to stabilize estimation algorithms for complex mechatronic systems, improving sensor selection by evaluating performance based on estimation accuracy, not just convenience.

Keywords:
extended Kalman filternon-linear modelsobservabilitysensor selectionstate estimation

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Area of Science:

  • Mechatronics
  • Control Systems Engineering
  • Signal Processing

Background:

  • Increasing mechatronic system complexity and safety regulations necessitate advanced estimation algorithms.
  • Complex models often suffer from observability issues and high computational demands.
  • Current sensor selection relies on heuristics rather than performance-based evaluation.

Purpose of the Study:

  • To develop a novel estimation and sensor selection approach for complex mechatronic systems.
  • To address challenges of observability and computational load in advanced estimation.
  • To enable cost-effective sensor evaluation based on estimation performance.

Main Methods:

  • Proposed an Extended Kalman Filter-based framework.
  • Stabilized the estimator Riccati equation using Singular Value Decomposition (SVD) projection onto an observable subspace.
  • Developed a sensor selection methodology ranking sensors by the error covariance of targeted quantities of interest.

Main Results:

  • Successfully stabilized the Riccati equation for unobservable and non-linear system models.
  • Demonstrated a method to evaluate sensor performance without costly experiments.
  • Validated the approach through numerical simulations and an automotive experimental case.

Conclusions:

  • The proposed method enhances estimation stability and accuracy for complex systems.
  • The sensor selection methodology offers a data-driven, cost-effective approach.
  • This work provides a robust framework for advanced mechatronic system design and optimization.