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

被引：3

作者：

Xia, Huafeng ^{[1
,3
]}

Wu, Zhengle ^{[1
]}

Xu, Sheng ^{[1
]}

Liu, Lijuan ^{[2
]}

Li, Yang ^{[1
]}

Zhou, Yin ^{[1
]}

机构：

[1] Taizhou Univ, Taizhou Elect Power Convers & Control Engn Technol, Taizhou, Peoples R China

[2] Wuxi Univ, Sch Internet Things Engn, Wuxi, Peoples R China

[3] Taizhou Univ, Taizhou Elect Power Convers & Control Engn Technol, Taizhou 225300, Peoples R China

来源：

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL | 2024年 / 34卷 / 02期

基金：

中国国家自然科学基金;

关键词：

interval-varying; least-squares method; maximum likelihood; missing data; nonlinear system; PARAMETER-ESTIMATION; IDENTIFICATION; ALGORITHM;

D O I：

10.1002/rnc.7031

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1312 / 1323

页数：12

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