Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems

Summary This article studies the identification problem of the nonlinear sandwich systems. For the sandwich system, because there are inner variables which cannot be measured in the information vector of the identification models, it is difficult to identify the nonlinear sandwich systems. In order...

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Veröffentlicht in:International journal of robust and nonlinear control Jg. 31; H. 1; S. 148 - 165
Hauptverfasser: Xu, Ling, Ding, Feng, Yang, Erfu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: 10.01.2021
Schlagworte:
auxiliary model
multiinnovation
nonlinear system
parameter estimation
sandwich system
ISSN:1049-8923, 1099-1239
Online-Zugang:Volltext
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Zusammenfassung:Summary This article studies the identification problem of the nonlinear sandwich systems. For the sandwich system, because there are inner variables which cannot be measured in the information vector of the identification models, it is difficult to identify the nonlinear sandwich systems. In order to overcome the difficulty, an auxiliary model is built to predict the estimates of inner variables by means of the output of the auxiliary model. For the purpose of employing the real‐time observed data, a cost function with dynamical data is constructed to capture on‐line information of the nonlinear sandwich system. On this basis, an auxiliary model stochastic gradient identification approach is proposed based on the gradient optimization. Moreover, an auxiliary model multiinnovation stochastic gradient estimation method is developed, which tends to enhance estimation accuracy by introducing more observed data dynamically. The numerical simulation is provided and the simulation results show that the proposed auxiliary model identification method is effective for the nonlinear sandwich systems.
Bibliographie:Funding information
National Natural Science Foundation of China, 61873111
ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.5266