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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Vydáno v:	IET control theory & applications Ročník 11; číslo 6; s. 857 - 869
Hlavní autoři:	Ma, Junxia, Xiong, Weili, Chen, Jing, Feng, Ding
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 14.04.2017
Témata:	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 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
ISSN:	1751-8644, 1751-8652
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1751-8644 1751-8652
DOI:	10.1049/iet-cta.2016.1033