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A fast convergent extended Kalman observer for nonlinear discrete-time systems

Guo, L. Z.; Zhu, Quanmin


L. Z. Guo

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Quan Zhu
Professor in Control Systems


A new extended Kalman observer for nonlinear discrete-time systems is derived. As always, stability and convergence rate depend directly on the matrices Qk and Rk and the initial state estimation error, the new observer has made improvement on these two aspects. A new criterion for the design of matrices Qk and Rk enables the observer to enlarge the convergent domain, an off-line-trained neural network can significantly reduce the initial state estimation error without increasing on-line computation burden. The integration of the techniques results in a stable and fast convergent observer. The observer performance is demonstrated with the estimation of flux and angular speed of an induction motor.


Guo, L. Z., & Zhu, Q. (2002). A fast convergent extended Kalman observer for nonlinear discrete-time systems. International Journal of Systems Science, 33(13), 1051-1058.

Journal Article Type Article
Publication Date Oct 20, 2002
Journal International Journal of Systems Science
Print ISSN 0020-7721
Publisher Taylor & Francis
Peer Reviewed Not Peer Reviewed
Volume 33
Issue 13
Pages 1051-1058
Public URL
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Additional Information Additional Information : This work adopts the novel approach of introducing a neural network into the Kalman observer to deal with the measurement of states of nonlinear systems. This work confirms the authors' contention that introduction of neural networks can relieve the complexity in computition and design of nonlinear dynamic systems.