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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Bibliographic Details
Published in:IET control theory & applications Vol. 11; no. 6; pp. 857 - 869
Main Authors: Ma, Junxia, Xiong, Weili, Chen, Jing, Feng, Ding
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 14.04.2017
Subjects:
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
Online Access:Get full text
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Description
Summary: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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ISSN:1751-8644
1751-8652
DOI:10.1049/iet-cta.2016.1033