Open AccessRecursive identification for Wiener non‐linear systems with non‐stationary disturbances
Author(s) -
Dong Shijian,
Yu Li,
Zhang WenAn,
Chen Bo
Publication year - 2019
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2018.6413
Subject(s) - control theory (sociology) , mathematics , white noise , recursive least squares filter , covariance matrix , covariance , system identification , mathematical optimization , computer science , algorithm , adaptive filter , statistics , artificial intelligence , control (management) , database , measure (data warehouse)
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.