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Nonlinear dynamical system identification using unscented Kalman filter

Kalman Filter is the most suitable choice for linear state space and Gaussian error distribution from decades. In general practical systems are not linear and Gaussian so these assumptions give inconsistent results. System Identification for nonlinear dynamical systems is a difficult task to perform...

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Main Authors: Rehman, M.J.U., Dass, S.C., Asirvadam, V.S.
Format: Conference or Workshop Item
Published: American Institute of Physics Inc. 2016
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85005982804&doi=10.1063%2f1.4968052&partnerID=40&md5=1ec8ef8fd2abdc8a50f1131227abb3c5
http://eprints.utp.edu.my/30637/
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spelling my.utp.eprints.306372022-03-25T07:13:10Z Nonlinear dynamical system identification using unscented Kalman filter Rehman, M.J.U. Dass, S.C. Asirvadam, V.S. Kalman Filter is the most suitable choice for linear state space and Gaussian error distribution from decades. In general practical systems are not linear and Gaussian so these assumptions give inconsistent results. System Identification for nonlinear dynamical systems is a difficult task to perform. Usually, Extended Kalman Filter (EKF) is used to deal with non-linearity in which Jacobian method is used for linearizing the system dynamics, But it has been observed that in highly non-linear environment performance of EKF is poor. Unscented Kalman Filter (UKF) is proposed here as a better option because instead of analytical linearization of state space, UKF performs statistical linearization by using sigma point calculated from deterministic samples. Formation of the posterior distribution is based on the propagation of mean and covariance through sigma points. © 2016 Author(s). American Institute of Physics Inc. 2016 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85005982804&doi=10.1063%2f1.4968052&partnerID=40&md5=1ec8ef8fd2abdc8a50f1131227abb3c5 Rehman, M.J.U. and Dass, S.C. and Asirvadam, V.S. (2016) Nonlinear dynamical system identification using unscented Kalman filter. In: UNSPECIFIED. http://eprints.utp.edu.my/30637/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Kalman Filter is the most suitable choice for linear state space and Gaussian error distribution from decades. In general practical systems are not linear and Gaussian so these assumptions give inconsistent results. System Identification for nonlinear dynamical systems is a difficult task to perform. Usually, Extended Kalman Filter (EKF) is used to deal with non-linearity in which Jacobian method is used for linearizing the system dynamics, But it has been observed that in highly non-linear environment performance of EKF is poor. Unscented Kalman Filter (UKF) is proposed here as a better option because instead of analytical linearization of state space, UKF performs statistical linearization by using sigma point calculated from deterministic samples. Formation of the posterior distribution is based on the propagation of mean and covariance through sigma points. © 2016 Author(s).
format Conference or Workshop Item
author Rehman, M.J.U.
Dass, S.C.
Asirvadam, V.S.
spellingShingle Rehman, M.J.U.
Dass, S.C.
Asirvadam, V.S.
Nonlinear dynamical system identification using unscented Kalman filter
author_facet Rehman, M.J.U.
Dass, S.C.
Asirvadam, V.S.
author_sort Rehman, M.J.U.
title Nonlinear dynamical system identification using unscented Kalman filter
title_short Nonlinear dynamical system identification using unscented Kalman filter
title_full Nonlinear dynamical system identification using unscented Kalman filter
title_fullStr Nonlinear dynamical system identification using unscented Kalman filter
title_full_unstemmed Nonlinear dynamical system identification using unscented Kalman filter
title_sort nonlinear dynamical system identification using unscented kalman filter
publisher American Institute of Physics Inc.
publishDate 2016
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85005982804&doi=10.1063%2f1.4968052&partnerID=40&md5=1ec8ef8fd2abdc8a50f1131227abb3c5
http://eprints.utp.edu.my/30637/
_version_ 1738657135545286656
score 13.211869

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