Refined instrumental variable parameter estimation of continuous-time Box-Jenkins models from irregularly sampled data

被引：7

作者：

Chen, Fengwei ^{[1
,2
,3
]}

Garnier, Hugues ^{[2
,3
]}

Gilson, Marion ^{[2
,3
]}

Aguero, Juan C. ^{[4
,5
]}

Liu, Tao ^{[1
]}

机构：

[1] Dalian Univ Technol, Sch Control Sci & Engn, Dalian 116024, Peoples R China

[2] Univ Lorraine, CRAN, UMR 7039, 2 Rue Jean Lamour, F-54519 Vandoeuvre Les Nancy, France

[3] CNRS, CRAN, UMR 7039, F-75700 Paris, France

[4] Univ Tecn Federico Santa Maria, Dept Elect, Ave Espana 1680, Valparaiso, Chile

[5] Univ Newcastle, Sch Elect Engn & Comp Sci, Callaghan, NSW 2308, Australia

来源：

IET CONTROL THEORY AND APPLICATIONS | 2017年 / 11卷 / 02期

关键词：

sampled data systems; parameter estimation; continuous time systems; iterative methods; computational efficiency; noise model; prediction error method; plant model; instrumental variable method; two-step iterative procedure; plant-noise model decomposition; Box-Jenkins structure; irregularly sampled data; continuous-time Box-Jenkins model parameter estimation; CONVERGENCE ANALYSIS; IDENTIFICATION; ALGORITHMS;

D O I：

10.1049/iet-cta.2016.0506

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 study investigates the estimation of continuous-time Box-Jenkins model parameters from irregularly sampled data. The Box-Jenkins structure has been successful in describing systems subject to coloured noise, since it contains two sub-models that feature the characteristics of both plant and noise systems. Based on plant-noise model decomposition, a two-step iterative procedure is proposed to solve the estimation problem, which consists of an instrumental variable method for the plant model and a prediction error method for the noise model. The proposed method is of low complexity and shows good estimation robustness and accuracy. Implementation issues are discussed to improve the computational efficiency. Numerical examples are presented to demonstrate the effectiveness of the proposed method.

引用

页码：291 / 300

页数：10

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