Laplace Distribution Based Online Identification of Linear Systems With Robust Recursive Expectation–Maximization Algorithm

The robust online identification problem of linear systems is considered in this article using a faster robust recursive expectation–maximization (RREM) framework. To improve the convergence rate, the outliers, which would deteriorate the identified models, are accommodated with a Laplace distributi...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics Vol. 19; no. 8; pp. 9028 - 9036
Main Authors: Chen, Xin, Zhao, Shunyi, Liu, Fei, Tao, Chongben
Format: Journal Article
Language:English
Published: Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.08.2023
Subjects:
Algorithms
Autoregressive models
Linear systems
Maximization
Optimization
Parameters
Recursive functions
Regression coefficients
Robustness
ISSN:1551-3203, 1941-0050
Online Access:Get full text
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Summary:The robust online identification problem of linear systems is considered in this article using a faster robust recursive expectation–maximization (RREM) framework. To improve the convergence rate, the outliers, which would deteriorate the identified models, are accommodated with a Laplace distribution instead of Student's [Formula Omitted]-distribution. Then, the recursive transformation of the maximum likelihood function is realized with a recursive [Formula Omitted]-function. The extensively recognized autoregressive exogenous (ARX) models are used for the description of general linear systems. As a result, the unknown parameters, including the regression coefficient vector of the ARX models, the variance of the noise without outliers, and the scale parameter of the Laplace distribution, are determined in a recursive manner. The performance of the proposed approach is tested with a simulated continuous fermentation reactor system example and a coupled-tank experiment.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2022.3225026