Comparison of SVSF-KF Adaptive Estimation Algorithms on an Electrohydrostatic Actuator Subject to a Fault

State estimation strategies are vital for obtaining knowledge of a dynamic system's state when faced with limited measurement capability, sensor noise, or uncertain system dynamics. The Kalman filter (KF) is one of the most widely recognized filters and provides the optimal solution for linear...

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Vydáno v:IEEE sensors journal Ročník 25; číslo 2; s. 2905 - 2920
Hlavní autoři: Goodman, Jacob, Hilal, Waleed, Gadsden, Stephen A., Eggleton, Charles D.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 15.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Actuators
Adaptation models
Adaptive algorithms
Adaptive estimation
Adaptive filtering
Adaptive systems
Algorithms
Boundary layers
Dynamical systems
Estimation
estimation theory
Fault detection
hydrostatic actuator
interacting multiple models (IMMs)
Kalman filter (KF)
Kalman filters
Measurement uncertainty
Noise
Noise measurement
Predictor-corrector methods
Robustness (mathematics)
Sensor phenomena and characterization
smooth variable structure filter (SVSF)
State estimation
Switches
System dynamics
Uncertainty
ISSN:1530-437X, 1558-1748
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Shrnutí:State estimation strategies are vital for obtaining knowledge of a dynamic system's state when faced with limited measurement capability, sensor noise, or uncertain system dynamics. The Kalman filter (KF) is one of the most widely recognized filters and provides the optimal solution for linear state estimation problems. The smooth variable structure filter (SVSF) is a model-based strategy that is also formulated as a predictor-corrector. Despite being a suboptimal estimator, the SVSF is highly robust to modeling uncertainties, errors, and system change. The combination of the SVSF with the KF (SVSF-KF) results in an adaptive estimation algorithm that provides an optimal KF estimate in normal operating conditions, and a robust SVSF estimate in the presence of faults or uncertainties. While effective in some cases, the SVSF-KF has been shown to suffer from several drawbacks associated with the time-varying smoothing boundary layer (SBL) and adaptive gain used to detect system change. Several new approaches have been proposed in recent years with the aim of improving the SVSF-KF's performance. Among these approaches is a novel gain formulation based on the normalized innovation squares (NISs), while another makes use of the interacting multiple model (IMM) framework. In this article, we review the newly proposed SVSF-KF formulations and compare their performance on an electrohydrostatic actuator (EHA) test case.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3452488