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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Vydané v:	IEEE sensors journal Ročník 25; číslo 2; s. 2905 - 2920
Hlavní autori:	Goodman, Jacob, Hilal, Waleed, Gadsden, Stephen A., Eggleton, Charles D.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 15.01.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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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Abstract	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.
AbstractList	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.
Author	Hilal, Waleed Eggleton, Charles D. Gadsden, Stephen A. Goodman, Jacob
Author_xml	– sequence: 1 givenname: Jacob surname: Goodman fullname: Goodman, Jacob email: jg4@umbc.edu organization: Department of Mechanical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA – sequence: 2 givenname: Waleed orcidid: 0000-0002-9164-165X surname: Hilal fullname: Hilal, Waleed email: hilalw@mcmaster.ca organization: Intelligent and Cognitive Engineering (ICE) Laboratory, McMaster University, Hamilton, ON, Canada – sequence: 3 givenname: Stephen A. orcidid: 0000-0003-3749-0878 surname: Gadsden fullname: Gadsden, Stephen A. email: gadsden@mcmaster.ca organization: Intelligent and Cognitive Engineering (ICE) Laboratory, McMaster University, Hamilton, ON, Canada – sequence: 4 givenname: Charles D. surname: Eggleton fullname: Eggleton, Charles D. email: eggleton@umbc.edu organization: Department of Mechanical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA
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SubjectTerms	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
Title	Comparison of SVSF-KF Adaptive Estimation Algorithms on an Electrohydrostatic Actuator Subject to a Fault
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