Hierarchical identification for multivariate Hammerstein systems by using the modified Kalman filter
The parameter estimation problem for multi-input multi-output Hammerstein systems is considered. For the Hammerstein model to be identified, its dynamic time-invariant subsystem is described by a controlled autoregressive model with a communication delay. The modified Kalman filter (MKF) algorithm i...
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| Veröffentlicht in: | IET control theory & applications Jg. 11; H. 6; S. 857 - 869 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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The Institution of Engineering and Technology
14.04.2017
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| ISSN: | 1751-8644, 1751-8652 |
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| Abstract | The parameter estimation problem for multi-input multi-output Hammerstein systems is considered. For the Hammerstein model to be identified, its dynamic time-invariant subsystem is described by a controlled autoregressive model with a communication delay. The modified Kalman filter (MKF) algorithm is derived to estimate the unknown intermediate variables in the system and the MKF-based recursive least squares (LS) algorithm is presented to estimate all the unknown parameters. Furthermore, the hierarchical identification is adopted to decompose the system into two fictitious subsystems: one containing the unknown parameters in the non-linear block and the other containing the unknown parameters in the linear subsystem. Then an MKF-based hierarchical LS algorithm is derived. The convergence analysis shows the performance of the presented algorithms. The numerical simulation results indicate that the proposed algorithms are effective. |
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| AbstractList | The parameter estimation problem for multi‐input multi‐output Hammerstein systems is considered. For the Hammerstein model to be identified, its dynamic time‐invariant subsystem is described by a controlled autoregressive model with a communication delay. The modified Kalman filter (MKF) algorithm is derived to estimate the unknown intermediate variables in the system and the MKF‐based recursive least squares (LS) algorithm is presented to estimate all the unknown parameters. Furthermore, the hierarchical identification is adopted to decompose the system into two fictitious subsystems: one containing the unknown parameters in the non‐linear block and the other containing the unknown parameters in the linear subsystem. Then an MKF‐based hierarchical LS algorithm is derived. The convergence analysis shows the performance of the presented algorithms. The numerical simulation results indicate that the proposed algorithms are effective. |
| Author | Xiong, Weili Feng, Ding Ma, Junxia Chen, Jing |
| Author_xml | – sequence: 1 givenname: Junxia surname: Ma fullname: Ma, Junxia organization: 2Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada T6G 2G6 – sequence: 2 givenname: Weili surname: Xiong fullname: Xiong, Weili organization: 1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, People's Republic of China – sequence: 3 givenname: Jing surname: Chen fullname: Chen, Jing organization: 1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, People's Republic of China – sequence: 4 givenname: Ding surname: Feng fullname: Feng, Ding email: fding@jiangnan.edu.cn organization: 3School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, People's Republic of China |
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| Copyright | The Institution of Engineering and Technology 2021 The Authors. IET Control Theory & Applications published by John Wiley & Sons, Ltd. on behalf of The Institution of Engineering and Technology |
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| Keywords | MKF-based recursive least squares algorithm least squares approximations controlled autoregressive model dynamic time-invariant subsystem hierarchical identification autoregressive processes modified Kalman filter communication delay multivariate Hammerstein system MIMO systems convergence analysis MKF-based hierarchical LS algorithm delays parameter estimation multiinput multioutput Hammerstein system Kalman filters |
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| SubjectTerms | Algorithms autoregressive processes communication delay controlled autoregressive model convergence analysis delays dynamic time‐invariant subsystem Dynamical systems Estimates hierarchical identification Kalman filters least squares approximations Mathematical models MIMO systems MKF‐based hierarchical LS algorithm MKF‐based recursive least squares algorithm modified Kalman filter multiinput multioutput Hammerstein system multivariate Hammerstein system Parameter estimation Parameter identification Parameter modification Parameters Research Article |
| Title | Hierarchical identification for multivariate Hammerstein systems by using the modified Kalman filter |
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