Multi-innovation gradient parameter estimation for multivariable systems based on the maximum likelihood principle

被引：6

作者：

Xia, Huafeng ^{[1
]}

Xu, Sheng ^{[1
]}

Zhou, Cheng ^{[1
]}

Chen, Feiyan ^{[2
]}

机构：

[1] Taizhou Univ, Taizhou Elect Power Convers & Control Engn Techno, Taizhou 225300, Peoples R China

[2] Xian Jiaotong Liverpool Univ, Sch Sci, Suzhou, Peoples R China

来源：

OPTIMAL CONTROL APPLICATIONS & METHODS | 2022年 / 43卷 / 01期

基金：

中国国家自然科学基金;

关键词：

decomposition technique; maximum likelihood; multi-innovation identification theory; multivariable system; parameter estimation; RECURSIVE LEAST-SQUARES; ITERATIVE ESTIMATION; IDENTIFICATION ALGORITHM; COLLISION-AVOIDANCE; NONLINEAR-SYSTEMS; TRACKING CONTROL; FAULT-DIAGNOSIS; STATE; MODEL; TIME;

D O I：

10.1002/oca.2766

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This article considers the parameter estimation problems of linear multivariable systems with unknown disturbances. For the parameter matrices in the multivariable systems, the model decomposition technique is used to reduce the computational complexity by decomposing the multivariable system into several subsystems with only the parameter vectors. By means of the negative gradient search, a decomposition-based maximum likelihood recursive extended stochastic gradient algorithm is derived. In order to improve the parameter estimation accuracy, by introducing the multi-innovation identification theory, a decomposition-based maximum likelihood multi-innovation extended stochastic gradient algorithm is proposed. The simulation results illustrate the effectiveness of the proposed algorithms.

引用

页码：106 / 122

页数：17

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