Sliding Window Iterative Identification for Nonlinear Closed‐Loop Systems Based on the Maximum Likelihood Principle

ABSTRACT The parameter estimation problem for the nonlinear closed‐loop systems with moving average noise is considered in this article. For purpose of overcoming the difficulty that the dynamic linear module and the static nonlinear module in nonlinear closed‐loop systems lead to identification com...

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Bibliographic Details
Published inInternational journal of robust and nonlinear control Vol. 35; no. 3; pp. 1100 - 1116
Main Authors Liu, Lijuan, Li, Fu, Liu, Wei, Xia, Huafeng
Format Journal Article
LanguageEnglish
Published Hoboken, USA John Wiley & Sons, Inc 01.02.2025
Wiley Subscription Services, Inc
Subjects
Algorithms
Iterative algorithms
iterative identification
maximum likelihood
Maximum likelihood estimates
Maximum likelihood estimation
Modules
nonlinear closed‐loop system
Nonlinear systems
Parameter estimation
Parameter identification
Simulation
Sliding
sliding window
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Summary:ABSTRACT The parameter estimation problem for the nonlinear closed‐loop systems with moving average noise is considered in this article. For purpose of overcoming the difficulty that the dynamic linear module and the static nonlinear module in nonlinear closed‐loop systems lead to identification complexity issues, the unknown parameters from both linear and nonlinear modules are included in a parameter vector by use of the key term separation technique. Furthermore, an sliding window maximum likelihood least squares iterative algorithm and an sliding window maximum likelihood gradient iterative algorithm are derived to estimate the unknown parameters. The numerical simulation indicates the efficiency of the proposed algorithms.
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ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.7705