An adaptive extended Kalman filter using artificial neural networks

Develops an adaptive state-estimation technique using artificial neural networks, referred to as a neuro-observer. The neuro-observer is an extended Kalman filter structure that has its state-coupling function augmented by an artificial neural network that captures the unmodeled dynamics. The neural...

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Bibliographic Details
Published inProceedings of 1995 34th IEEE Conference on Decision and Control Vol. 2; pp. 1852 - 1856 vol.2
Main Authors Stubberud, S.C., Lobbia, R.N., Owen, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE Control Systems Society 1995
Subjects
Additive noise
Artificial neural networks
Feedback control
Feedback loop
Jacobian matrices
Multi-layer neural network
Neural networks
Noise measurement
Optimal control
State estimation
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ISBN0780326857
9780780326859
ISSN0191-2216
DOI10.1109/CDC.1995.480611

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Summary:Develops an adaptive state-estimation technique using artificial neural networks, referred to as a neuro-observer. The neuro-observer is an extended Kalman filter structure that has its state-coupling function augmented by an artificial neural network that captures the unmodeled dynamics. The neural network of the neuro-observer trains on-line using an extended Kalman filter training paradigm. Improvement in the system model then provides for a more accurate state estimate in the feedback loop, thus enhancing the control signal so that the system behaves in a closer to optimal fashion.
ISBN:0780326857
9780780326859
ISSN:0191-2216
DOI:10.1109/CDC.1995.480611