Nonlinear moving horizon estimation in the presence of bounded disturbances

Research output: Contribution to journalArticleResearchpeer review

External Research Organisations

  • University of Stuttgart
View graph of relations

Details

Original languageEnglish
Pages (from-to)306-314
Number of pages9
JournalAutomatica
Volume79
Early online date6 Mar 2017
Publication statusPublished - 1 May 2017
Externally publishedYes

Abstract

In this paper, we propose a new moving horizon estimator for nonlinear detectable systems. Similar to a recently proposed full information estimator, the corresponding cost function contains an additional max-term compared to more standard least-squares type approaches. We show that robust global asymptotic stability in case of bounded disturbances and convergence of the estimation error in case of vanishing disturbances can be established. Second, we show that the same results hold for a standard least-squares type moving horizon estimator, which so far has not been proven even in the full information estimation case. An additional advantage of the proposed estimators is that a suitable prior weighting appearing in the cost function can explicitly be determined offline, which is not the case in various existing approaches.

Keywords

    Moving horizon estimation, Nonlinear state estimation, Nonlinear systems, Robust stability

ASJC Scopus subject areas

Cite this

Nonlinear moving horizon estimation in the presence of bounded disturbances. / Müller, Matthias A.
In: Automatica, Vol. 79, 01.05.2017, p. 306-314.

Research output: Contribution to journalArticleResearchpeer review

Müller MA. Nonlinear moving horizon estimation in the presence of bounded disturbances. Automatica. 2017 May 1;79:306-314. Epub 2017 Mar 6. doi: 10.1016/j.automatica.2017.01.033
Download
@article{4c93ff0a856a484d9e4ffb2cccb1f90d,
title = "Nonlinear moving horizon estimation in the presence of bounded disturbances",
abstract = "In this paper, we propose a new moving horizon estimator for nonlinear detectable systems. Similar to a recently proposed full information estimator, the corresponding cost function contains an additional max-term compared to more standard least-squares type approaches. We show that robust global asymptotic stability in case of bounded disturbances and convergence of the estimation error in case of vanishing disturbances can be established. Second, we show that the same results hold for a standard least-squares type moving horizon estimator, which so far has not been proven even in the full information estimation case. An additional advantage of the proposed estimators is that a suitable prior weighting appearing in the cost function can explicitly be determined offline, which is not the case in various existing approaches.",
keywords = "Moving horizon estimation, Nonlinear state estimation, Nonlinear systems, Robust stability",
author = "M{\"u}ller, {Matthias A.}",
note = "Publisher Copyright: {\textcopyright} 2017 Elsevier Ltd Copyright: Copyright 2017 Elsevier B.V., All rights reserved.",
year = "2017",
month = may,
day = "1",
doi = "10.1016/j.automatica.2017.01.033",
language = "English",
volume = "79",
pages = "306--314",
journal = "Automatica",
issn = "0005-1098",
publisher = "Elsevier Ltd.",

}

Download

TY - JOUR

T1 - Nonlinear moving horizon estimation in the presence of bounded disturbances

AU - Müller, Matthias A.

N1 - Publisher Copyright: © 2017 Elsevier Ltd Copyright: Copyright 2017 Elsevier B.V., All rights reserved.

PY - 2017/5/1

Y1 - 2017/5/1

N2 - In this paper, we propose a new moving horizon estimator for nonlinear detectable systems. Similar to a recently proposed full information estimator, the corresponding cost function contains an additional max-term compared to more standard least-squares type approaches. We show that robust global asymptotic stability in case of bounded disturbances and convergence of the estimation error in case of vanishing disturbances can be established. Second, we show that the same results hold for a standard least-squares type moving horizon estimator, which so far has not been proven even in the full information estimation case. An additional advantage of the proposed estimators is that a suitable prior weighting appearing in the cost function can explicitly be determined offline, which is not the case in various existing approaches.

AB - In this paper, we propose a new moving horizon estimator for nonlinear detectable systems. Similar to a recently proposed full information estimator, the corresponding cost function contains an additional max-term compared to more standard least-squares type approaches. We show that robust global asymptotic stability in case of bounded disturbances and convergence of the estimation error in case of vanishing disturbances can be established. Second, we show that the same results hold for a standard least-squares type moving horizon estimator, which so far has not been proven even in the full information estimation case. An additional advantage of the proposed estimators is that a suitable prior weighting appearing in the cost function can explicitly be determined offline, which is not the case in various existing approaches.

KW - Moving horizon estimation

KW - Nonlinear state estimation

KW - Nonlinear systems

KW - Robust stability

UR - http://www.scopus.com/inward/record.url?scp=85014160082&partnerID=8YFLogxK

U2 - 10.1016/j.automatica.2017.01.033

DO - 10.1016/j.automatica.2017.01.033

M3 - Article

VL - 79

SP - 306

EP - 314

JO - Automatica

JF - Automatica

SN - 0005-1098

ER -

By the same author(s)