Least square algorithm based on bias compensated principle for parameter estimation of canonical state space model

被引：5

作者：

Liu, Longlong ^{[1
]}

Long, Zhen ^{[1
]}

Azar, Ahmad Taher ^{[2
,3
]}

Zhu, Quanmin ^{[4
]}

Ibraheem, Ibraheem Kasim ^{[5
]}

Humaidi, Amjad J. ^{[6
]}

机构：

[1] Ocean Univ China, Sch Math Sci, Qingdao, Peoples R China

[2] Prince Sultan Univ, Coll Comp & Informat Sci, Riyadh, Saudi Arabia

[3] Benha Univ, Fac Comp & Artificial Intelligence, Banha, Egypt

[4] Univ West England, Dept Engn Design & Math, Bristol, Avon, England

[5] Dijlah Univ Coll, Dept Comp Tech Engn, Baghdad, Iraq

[6] Univ Technol Baghdad, Control & Syst Engn Dept, Al Sina St, Baghdad 19006, Iraq

来源：

MEASUREMENT & CONTROL | 2022年 / 55卷 / 5-6期

关键词：

least square algorithm; bias compensated principle; parameter estimation; canonical state space model; IDENTIFICATION;

D O I：

10.1177/00202940211064179

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Due to the existence of system noise and unknown state variables, it is difficult to realize unbiased estimation with minimum variance for the parameter estimation of canonical state space model. This paper presents a new least squares estimator based on bias compensation principle to solve this problem, transforms canonical state space into the form suitable for the least square algorithm, introduces an augmented parameter vector and an auxiliary variable, derives parameter estimation formula based on noise compensation, realizes the unbiased estimation, and gives the specific algorithm. A simulation example is provided to verify the effectiveness of the estimator.

引用

页码：330 / 339

页数：10

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