Combined state and least squares parameter estimation algorithms for dynamic systems

被引：225

作者：

Ding, Feng ^{[1
,2
]}

机构：

[1] Jiangnan Univ, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Peoples R China

[2] Jiangnan Univ, Control Sci & Engn Res Ctr, Wuxi 214122, Peoples R China

来源：

APPLIED MATHEMATICAL MODELLING | 2014年 / 38卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Dynamic system; Numerical algorithm; Least squares; Parameter estimation; Recursive identification; State space model; IDENTIFICATION METHODS; ITERATIVE ESTIMATION; PERFORMANCE ANALYSIS; MODEL;

D O I：

10.1016/j.apm.2013.06.007

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The control theory and automation technology cast the glory of our era. Highly integrated computer chip and automation products are changing our lives. Mathematical models and parameter estimation are basic for automatic control. This paper discusses the parameter estimation algorithm of establishing the mathematical models for dynamic systems and presents an estimated states based recursive least squares algorithm, and the states of the system are computed through the Kalman filter using the estimated parameters. A numerical example is provided to confirm the effectiveness of the proposed algorithm. (C) 2013 Elsevier Inc. All rights reserved.

引用

页码：403 / 412

页数：10

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