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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Veröffentlicht in:	IET control theory & applications Jg. 13; H. 16; S. 2648 - 2657
Hauptverfasser:	Dong, Shijian, Yu, Li, Zhang, Wen-An, Chen, Bo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 05.11.2019
Schlagworte:	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
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Beschreibung
Zusammenfassung:	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