A Neural Network Perspective to Extended Luenberger Observers
In this paper we investigate the use of adaptive extended Luenberger state estimators for general nonlinear and possibly time-varying systems. We identify the connection between the extended Luenberger observer and Grossberg's additive model for dynamic neural networks. The association between...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2002-02-01
|
| Series: | Measurement + Control |
| Online Access: | https://doi.org/10.1177/002029400203500103 |
| _version_ | 1818309069365051392 |
|---|---|
| author | Deniz Erdogmus A. Umut Genç José C. Príncipe |
| author_facet | Deniz Erdogmus A. Umut Genç José C. Príncipe |
| author_sort | Deniz Erdogmus |
| collection | DOAJ |
| description | In this paper we investigate the use of adaptive extended Luenberger state estimators for general nonlinear and possibly time-varying systems. We identify the connection between the extended Luenberger observer and Grossberg's additive model for dynamic neural networks. The association between dynamic neural networks and the Luenberger observer leads to an obvious modification on the proposed observer scheme that would allow handling state estimation for those systems whose dynamic equations are partially known or not known at all. The performance of the adaptive observer is demonstrated on a number of systems including an LTI system, the Van der Pol oscillator, the Lorenz attractor and a realistic partial gasoline engine model. |
| first_indexed | 2024-12-13T07:24:18Z |
| format | Article |
| id | doaj.art-0d680be7b2f348ca93b655edc2167a0a |
| institution | Directory Open Access Journal |
| issn | 0020-2940 |
| language | English |
| last_indexed | 2024-12-13T07:24:18Z |
| publishDate | 2002-02-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Measurement + Control |
| spelling | doaj.art-0d680be7b2f348ca93b655edc2167a0a2022-12-21T23:55:20ZengSAGE PublishingMeasurement + Control0020-29402002-02-013510.1177/002029400203500103A Neural Network Perspective to Extended Luenberger ObserversDeniz Erdogmus0A. Umut Genç1José C. Príncipe2 CNEL, Dept. of Electrical and Computer Engineering, University of Florida Dept. of Engineering, University of Cambridge CNEL, Dept. of Electrical and Computer Engineering, University of FloridaIn this paper we investigate the use of adaptive extended Luenberger state estimators for general nonlinear and possibly time-varying systems. We identify the connection between the extended Luenberger observer and Grossberg's additive model for dynamic neural networks. The association between dynamic neural networks and the Luenberger observer leads to an obvious modification on the proposed observer scheme that would allow handling state estimation for those systems whose dynamic equations are partially known or not known at all. The performance of the adaptive observer is demonstrated on a number of systems including an LTI system, the Van der Pol oscillator, the Lorenz attractor and a realistic partial gasoline engine model.https://doi.org/10.1177/002029400203500103 |
| spellingShingle | Deniz Erdogmus A. Umut Genç José C. Príncipe A Neural Network Perspective to Extended Luenberger Observers Measurement + Control |
| title | A Neural Network Perspective to Extended Luenberger Observers |
| title_full | A Neural Network Perspective to Extended Luenberger Observers |
| title_fullStr | A Neural Network Perspective to Extended Luenberger Observers |
| title_full_unstemmed | A Neural Network Perspective to Extended Luenberger Observers |
| title_short | A Neural Network Perspective to Extended Luenberger Observers |
| title_sort | neural network perspective to extended luenberger observers |
| url | https://doi.org/10.1177/002029400203500103 |
| work_keys_str_mv | AT denizerdogmus aneuralnetworkperspectivetoextendedluenbergerobservers AT aumutgenc aneuralnetworkperspectivetoextendedluenbergerobservers AT josecprincipe aneuralnetworkperspectivetoextendedluenbergerobservers AT denizerdogmus neuralnetworkperspectivetoextendedluenbergerobservers AT aumutgenc neuralnetworkperspectivetoextendedluenbergerobservers AT josecprincipe neuralnetworkperspectivetoextendedluenbergerobservers |