Auxiliary model‐based recursive least squares algorithm for two‐input single‐output Hammerstein output‐error moving average systems by using the hierarchical identification principle

This article considers the parameter estimation problems of two‐input single‐output Hammerstein output‐error moving average systems. The system is decomposed into two subsystems based on the hierarchical principle. The first model is used to identify the linear parameters and the parameters of the u...

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Bibliographic Details
Published in:International journal of robust and nonlinear control Vol. 32; no. 13; pp. 7575 - 7593
Main Authors: Liu, Jian, Ji, Yan
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 10.09.2022
Subjects:
Algorithms
auxiliary model
Least squares
Mathematical models
Model accuracy
multi‐innovation identification theory
nonlinear system
Parameter estimation
Parameter identification
Principles
recursive identification
Subsystems
ISSN:1049-8923, 1099-1239
Online Access:Get full text
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Summary:This article considers the parameter estimation problems of two‐input single‐output Hammerstein output‐error moving average systems. The system is decomposed into two subsystems based on the hierarchical principle. The first model is used to identify the linear parameters and the parameters of the unknown measurable information vector. The second model is for identifying non‐linear parameters. By using the auxiliary model, we introduce a forgetting factor to improve the parameter estimation accuracy. The auxiliary model‐based forgetting factor recursive least squares algorithm and the auxiliary model‐based forgetting factor multi‐innovation recursive least squares algorithm are presented. The simulation results indicate that the proposed algorithms are effective.
Bibliography:Funding information
National Natural Science Foundation of China, Grant/Award Numbers: 61472195; 61773356; Natural Science Foundation of Shandong Province, Grant/Award Number: ZR2020MF160
ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.6227