Recursive identification for Wiener non-linear systems with non-stationary disturbances

This study investigates the robust recursive identification problem for Wiener non-linear systems with non-stationary disturbance and measurement white Gaussian noise. Non-stationary disturbances cannot be eliminated by conventional statistical strategy. To overcome such problem, the non-stationary...

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Bibliographic Details
Published in:IET control theory & applications Vol. 13; no. 16; pp. 2648 - 2657
Main Authors: Dong, Shijian, Yu, Li, Zhang, Wen-An, Chen, Bo
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 05.11.2019
Subjects:
adaptive control
asymptotic convergence conditions
covariance matrices
covariance matrix
discrete time systems
discrete‐time recursive least‐squares algorithm
dynamic tracking ability
filtering theory
Gaussian noise
identification
information vector
least squares approximations
measurement white Gaussian noise
nonlinear control systems
nonstationary disturbance signal
parameter estimation
parameter vector
persistent excitation condition
recursive estimation
Research Article
robust recursive identification problem
statistical analysis
steady asymptotic convergence
stochastic processes
stochastic statistical theory
time‐invariant system parameters
white noise
Wiener nonlinear systems
steady asymptotic convergence
Wiener nonlinear systems
stochastic statistical theory
robust recursive identification problem
covariance matrix
white noise
identification
measurement white Gaussian noise
Gaussian noise
time-invariant system parameters
recursive estimation
parameter estimation
stochastic processes
covariance matrices
least squares approximations
nonstationary disturbance signal
discrete time systems
dynamic tracking ability
filtering theory
asymptotic convergence conditions
adaptive control
discrete-time recursive least-squares algorithm
parameter vector
nonlinear control systems
information vector
statistical analysis
persistent excitation condition
ISSN:1751-8644, 1751-8652
Online Access:Get full text
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Description
Summary:This study investigates the robust recursive identification problem for Wiener non-linear systems with non-stationary disturbance and measurement white Gaussian noise. Non-stationary disturbances cannot be eliminated by conventional statistical strategy. To overcome such problem, the non-stationary disturbance signal is modelled as a dynamic parameter with a small change rate which is to be estimated step. Utilising the forgetting factor (FF) and the auxiliary model strategy, a discrete-time recursive least-squares algorithm is designed by augmenting the parameter vector and the information vector. To facilitate the steady asymptotic convergence of the time-invariant system parameters and the dynamic tracking ability of the disturbance, two adaptive FFs are proposed to construct a new matrix FF scheme that is used to update the covariance matrix. Moreover, several asymptotic convergence conditions are also derived based on the stochastic statistical theory under persistent excitation condition. Finally, one illustrative example with non-stationary disturbance is employed to show the effectiveness and advantages of the proposed algorithms.
ISSN:1751-8644
1751-8652
DOI:10.1049/iet-cta.2018.6413