Using horizon estimation and nonlinear optimization for grey-box identification

被引：9

作者：

Isaksson, Alf J. ^{[1
,2
]}

Sjoberg, Johan ^{[2
,3
]}

Tornqvist, David ^{[4
]}

Ljung, Lennart ^{[2
]}

Kok, Manon ^{[2
]}

机构：

[1] Corp Res, ABB AB, SE-72178 Vasteras, Sweden

[2] Linkoping Univ, Dept Elect Engn, SE-58183 Linkoping, Sweden

[3] Volvo Construct Equipment, SE-63185 Eskilstuna, Sweden

[4] SenionLab AB, S-58330 Linkoping, Sweden

来源：

JOURNAL OF PROCESS CONTROL | 2015年 / 30卷

基金：

瑞典研究理事会; 欧洲研究理事会;

关键词：

System identification; State estimation; Parameter estimation; Optimization; Nonlinear systems; Kalman filtering; Moving horizon estimation; Model predictive control; STATE ESTIMATION; PARAMETER; ALGORITHM;

D O I：

10.1016/j.jprocont.2014.12.008

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

An established method for grey-box identification is to use maximum-likelihood estimation for the nonlinear case implemented via extended Kalman filtering. In applications of (nonlinear) model predictive control a more and more common approach for the state estimation is to use moving horizon estimation, which employs (nonlinear) optimization directly on a model for a whole batch of data. This paper shows that, in the linear case, horizon estimation may also be used for joint parameter estimation and state estimation, as long as a bias correction based on the Kalman filter is included. For the nonlinear case two special cases are presented where the bias correction can be determined without approximation. A procedure how to approximate the bias correction for general nonlinear systems is also outlined. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：69 / 79

页数：11

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