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Formulation of the Alpha Sliding Innovation Filter: A Robust Linear Estimation Strategy.

Mohammad AlShabi1, Stephen Andrew Gadsden2

  • 1Department of Mechanical & Nuclear Engineering, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates.

Sensors (Basel, Switzerland)
|November 26, 2022
PubMed
Summary
This summary is machine-generated.

A new alpha sliding innovation filter (ASIF) enhances estimation performance by incorporating a forgetting factor. This robust filter improves accuracy in systems like thermometers and actuators, even with uncertainties.

Keywords:
Kalman filtersestimation theoryforgetting factorrobustnesssliding innovation filter

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Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • Estimation Theory

Background:

  • The sliding innovation filter (SIF) is an estimation strategy utilizing measurement error as a switching hyperplane.
  • SIF offers a robust and stable, albeit sub-optimal, estimation approach.
  • Existing SIF methods can be further optimized for improved performance.

Purpose of the Study:

  • To introduce a reformulated sliding innovation filter, termed the alpha sliding innovation filter (ASIF).
  • To enhance the estimation performance and robustness of the SIF by incorporating a forgetting factor.
  • To validate the ASIF's effectiveness across various dynamic systems.

Main Methods:

  • Reformulation of the sliding innovation filter (SIF) by integrating a forgetting factor.
  • Development and implementation of the alpha sliding innovation filter (ASIF).
  • Experimental application and testing of the ASIF on a first-order thermometer, a second-order spring-mass-damper, and a third-order electrohydrostatic actuator (EHA).

Main Results:

  • The ASIF demonstrates significantly improved estimation performance compared to the standard SIF.
  • The filter maintains robustness against modeling uncertainties and external disturbances.
  • Accurate state estimation was achieved across diverse system orders and complexities.

Conclusions:

  • The alpha sliding innovation filter (ASIF) represents a significant advancement in estimation techniques.
  • The inclusion of a forgetting factor is crucial for enhancing SIF performance.
  • ASIF provides a reliable and accurate estimation solution for complex engineering systems.