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: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
American Institute of Physics Inc.
2016
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| 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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| Institution: | Universiti Teknologi Petronas |
| Summary: | 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). |
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