Maximum likelihood‐based gradient estimation for multivariable nonlinear systems using the multiinnovation identification theory

Summary This article considers the identification problems of multivariable input nonlinear systems with unmeasured disturbances. For the identification difficulty caused by the crossproducts between the parameters of the linear block and the nonlinear block, the key term separation technique is ado...

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Bibliographic Details
Published in:International journal of robust and nonlinear control Vol. 30; no. 14; pp. 5446 - 5463
Main Authors: Xia, Huafeng, Ji, Yan, Xu, Ling, Alsaedi, Ahmed, Hayat, Tasawar
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 25.09.2020
Subjects:
Algorithms
Computer simulation
Identification
maximum likelihood
Maximum likelihood estimation
multiinnovation identification theory
nonlinear system
Nonlinear systems
Parameter estimation
Parameter identification
Separation
ISSN:1049-8923, 1099-1239
Online Access:Get full text
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Summary:Summary This article considers the identification problems of multivariable input nonlinear systems with unmeasured disturbances. For the identification difficulty caused by the crossproducts between the parameters of the linear block and the nonlinear block, the key term separation technique is adopted to separate the parameters of the nonlinear block from the parameters of the linear block. By combining the model decomposition technique and the hierarchical identification principle, a key term separation‐based maximum likelihood recursive extended stochastic gradient algorithm with reduced computational complexity is presented to estimate all the parameters directly. By introducing the multiinnovation identification theory, a key term separation‐based maximum likelihood multiinnovation extended stochastic gradient algorithm is proposed to improve the parameter estimation accuracy. The simulation results illustrate the effectiveness of the proposed methods.
Bibliography:Funding information
National Natural Science Foundation of China, 51609164
ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.5086