Auxiliary model‐based interval‐varying maximum likelihood estimation for nonlinear systems with missing data

The identification problem of nonlinear system with missing data is focused in this article. In order to overcome the system unavailable outputs, an auxiliary model‐based interval‐varying recursive identification method is derived by changing the sampling interval and substituting the missing output...

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Bibliographic Details
Published in:International journal of robust and nonlinear control 2024-01, Vol.34 (2), p.1312-1323
Main Authors: Xia, Huafeng, Wu, Zhengle, Xu, Sheng, Liu, Lijuan, Li, Yang, Zhou, Yin
Format: Article
Language:English
Subjects:
Continuously stirred tank reactors
Identification methods
interval‐varying
least‐squares method
Mathematical models
maximum likelihood
Maximum likelihood estimation
Missing data
nonlinear system
Nonlinear systems
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Summary:The identification problem of nonlinear system with missing data is focused in this article. In order to overcome the system unavailable outputs, an auxiliary model‐based interval‐varying recursive identification method is derived by changing the sampling interval and substituting the missing output with the output of an auxiliary model. Based on the maximum likelihood principle and the least‐squares method, a maximum likelihood‐based interval‐varying recursive least‐squares method is investigated. The validity of the proposed maximum likelihood method is tested by a numerical simulation example and a practical continuous stirred tank reactor (CSTR) process.
ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.7031