Auxiliary model‐based multi‐innovation recursive identification algorithms for an input nonlinear controlled autoregressive moving average system with variable‐gain nonlinearity

Summary For the parameter estimation problem of an input nonlinear controlled autoregressive moving average system with variable‐gain nonlinearity, this article gives an analytical form of the variable‐gain nonlinearity by introducing an appropriate switching function and derives an auxiliary model‐...

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Bibliographic Details
Published in:International journal of adaptive control and signal processing Vol. 36; no. 3; pp. 521 - 540
Main Authors: Fan, Yamin, Liu, Ximei
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 01.03.2022
Subjects:
Algorithms
Autoregressive moving average
gradient search
Innovations
input nonlinear system
least‐squares
Mathematical models
Model accuracy
multi‐innovation identification
Nonlinear control
Nonlinearity
Parameter estimation
variable‐gain nonlinearity
ISSN:0890-6327, 1099-1115
Online Access:Get full text
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Summary:Summary For the parameter estimation problem of an input nonlinear controlled autoregressive moving average system with variable‐gain nonlinearity, this article gives an analytical form of the variable‐gain nonlinearity by introducing an appropriate switching function and derives an auxiliary model‐based extended stochastic gradient algorithm with a forgetting factor and an auxiliary model‐based recursive extended least‐squares algorithm. For the sake of improving the parameter estimation accuracy, an auxiliary model‐based multi‐innovation extended stochastic gradient algorithm with a forgetting factor and an auxiliary model‐based multi‐innovation recursive extended least‐squares algorithm are presented by utilizing the multi‐innovation identification theory. The simulation results confirm the effectiveness of the proposed algorithms and show that the auxiliary model‐based multi‐innovation recursive identification algorithms have higher identification accuracy compared with the other two algorithms.
Bibliography:Funding information
National Natural Science Foundation of China, 61472195
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0890-6327
1099-1115
DOI:10.1002/acs.3354