State filtering-based least squares parameter estimation for bilinear systems using the hierarchical identification principle

This study presents a combined parameter and state estimation algorithm for a bilinear system described by its observer canonical state-space model based on the hierarchical identification principle. The Kalman filter is known as the best state filter for linear systems, but not applicable for bilin...

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Veröffentlicht in:	IET control theory & applications Jg. 12; H. 12; S. 1704 - 1713
Hauptverfasser:	Zhang, Xiao, Ding, Feng, Xu, Ling, Yang, Erfu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 14.08.2018
Schlagworte:	bilinear state observer bilinear system bilinear systems BSO‐based forgetting factor recursive least squares algorithm BSO‐RLS algorithm combined parameter decomposition–coordination principle extremum principle filtering theory gradient methods hierarchical identification principle hierarchical least squares algorithm identification Kalman filter Kalman filters least squares approximations linear systems observer canonical state‐space model observers parameter estimation parameter tracking capability recursive estimation Research Article squares parameter estimation state estimation state filter state‐space methods BSO-based forgetting factor recursive least squares algorithm Kalman filter hierarchical least squares algorithm combined parameter parameter tracking capability state filter extremum principle identification bilinear system recursive estimation parameter estimation Kalman filters gradient methods decomposition–coordination principle observer canonical state-space model least squares approximations filtering theory bilinear systems linear systems squares parameter estimation hierarchical identification principle state estimation bilinear state observer BSO-RLS algorithm observers state-space methods
ISSN:	1751-8644, 1751-8652
Online-Zugang:	Volltext
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