Identification of a Class of Non-linear State Space Models using RPE Techniques

Wei-Wu Zhou; Mogens Blanke

doi:10.1109/9.16421

Identification of a Class of Non-linear State Space Models using RPE Techniques

Wei-Wu Zhou
, Mogens Blanke

Technical University of Denmark

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Abstract

The RPE (recursive prediction error) method in state-space form is developed in the nonlinear systems 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
Journal	I E E E Transactions on Automatic Control
Volume	34
Issue number	3
Pages (from-to)	312-316
ISSN	0018-9286
DOIs	https://doi.org/10.1109/9.16421
Publication status	Published - 1989

Bibliographical note

Copyright: 1989 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

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abstract = "The RPE (recursive prediction error) method in state-space form is developed in the nonlinear systems 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",

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PY - 1989

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N2 - The RPE (recursive prediction error) method in state-space form is developed in the nonlinear systems 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 RPE (recursive prediction error) method in state-space form is developed in the nonlinear systems 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

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Identification of a Class of Non-linear State Space Models using RPE Techniques

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