Identification of a class of nonlinear state-space models using RPE techniques

W. W. Zhou; Mogens Blanke

doi:10.1109/CDC.1986.267185

Identification of a class of nonlinear state-space models using RPE techniques

W. W. Zhou, Mogens Blanke

Technical University of Denmark

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

572 Downloads (Pure)

Abstract

The recursive prediction error methods in state-space form have been efficiently used as parameter identifiers for linear systems, and especially Ljung's innovations filter using a Newton search direction has proved to be quite ideal. In this paper, the RPE method in state-space form is developed to the nonlinear case and extended to include the exact form of a nonlinearity, thus enabling structure preservation for certain classes of nonlinear systems. Both the discrete and the continuous-discrete versions of the algorithm in an innovations model are investigated, and a nonlinear simulation example shows a quite convincing performance of the filter as combined parameter and state estimator.

Original language	English
Title of host publication	25th IEEE Conference on Decision and Control
Volume	Volume 25
Publisher	IEEE
Publication date	1986
Pages	1637-1642
DOIs	https://doi.org/10.1109/CDC.1986.267185
Publication status	Published - 1986
Event	25th IEEE Conference on Decision and Control - Athens, Greece Duration: 10 Dec 1986 → 12 Dec 1986 Conference number: 25

Conference

Conference	25th IEEE Conference on Decision and Control
Number	25
Country/Territory	Greece
City	Athens
Period	10/12/1986 → 12/12/1986

Bibliographical note

Copyright: 1986 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

Access to Document

10.1109/CDC.1986.267185

BlankeFinal published version, 367 KB

OpenUrl availability

Full text

Cite this

@inproceedings{8c820322a9154ac9a72842e75639295c,

title = "Identification of a class of nonlinear state-space models using RPE techniques",

abstract = "The recursive prediction error methods in state-space form have been efficiently used as parameter identifiers for linear systems, and especially Ljung's innovations filter using a Newton search direction has proved to be quite ideal. In this paper, the RPE method in state-space form is developed to the nonlinear case and extended to include the exact form of a nonlinearity, thus enabling structure preservation for certain classes of nonlinear systems. Both the discrete and the continuous-discrete versions of the algorithm in an innovations model are investigated, and a nonlinear simulation example shows a quite convincing performance of the filter as combined parameter and state estimator.",

author = "Zhou, {W. W.} and Mogens Blanke",

note = "Copyright: 1986 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE; 25th IEEE Conference on Decision and Control ; Conference date: 10-12-1986 Through 12-12-1986",

year = "1986",

doi = "10.1109/CDC.1986.267185",

language = "English",

volume = "Volume 25",

pages = "1637--1642",

booktitle = "25th IEEE Conference on Decision and Control",

publisher = "IEEE",

address = "United States",

}

TY - GEN

T1 - Identification of a class of nonlinear state-space models using RPE techniques

AU - Zhou, W. W.

AU - Blanke, Mogens

N1 - Conference code: 25

PY - 1986

Y1 - 1986

N2 - The recursive prediction error methods in state-space form have been efficiently used as parameter identifiers for linear systems, and especially Ljung's innovations filter using a Newton search direction has proved to be quite ideal. In this paper, the RPE method in state-space form is developed to the nonlinear case and extended to include the exact form of a nonlinearity, thus enabling structure preservation for certain classes of nonlinear systems. Both the discrete and the continuous-discrete versions of the algorithm in an innovations model are investigated, and a nonlinear simulation example shows a quite convincing performance of the filter as combined parameter and state estimator.

AB - The recursive prediction error methods in state-space form have been efficiently used as parameter identifiers for linear systems, and especially Ljung's innovations filter using a Newton search direction has proved to be quite ideal. In this paper, the RPE method in state-space form is developed to the nonlinear case and extended to include the exact form of a nonlinearity, thus enabling structure preservation for certain classes of nonlinear systems. Both the discrete and the continuous-discrete versions of the algorithm in an innovations model are investigated, and a nonlinear simulation example shows a quite convincing performance of the filter as combined parameter and state estimator.

U2 - 10.1109/CDC.1986.267185

DO - 10.1109/CDC.1986.267185

M3 - Article in proceedings

VL - Volume 25

SP - 1637

EP - 1642

BT - 25th IEEE Conference on Decision and Control

PB - IEEE

T2 - 25th IEEE Conference on Decision and Control

Y2 - 10 December 1986 through 12 December 1986

ER -

Identification of a class of nonlinear state-space models using RPE techniques

Abstract

Conference

Bibliographical note

Access to Document

OpenUrl availability

Fingerprint

Cite this