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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Bibliographic Details
Published in:	IEEE sensors journal Vol. 25; no. 2; pp. 2905 - 2920
Main Authors:	Goodman, Jacob, Hilal, Waleed, Gadsden, Stephen A., Eggleton, Charles D.
Format:	Journal Article
Language:	English
Published:	New York IEEE 15.01.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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