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

Guo, L. Z.; Zhu, Quanmin

Abstract

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.

• Journal Article Type: Article
• Publication Date: Oct 20, 2002
• Journal: International Journal of Systems Science
• Print ISSN: 0020-7721
• Electronic ISSN: 1464-5319
• Publisher: Taylor & Francis
• Peer Reviewed: Not Peer Reviewed
• Volume: 33
• Issue: 13
• Pages: 1051-1058
• DOI: https://doi.org/10.1080/0020772021000046225
• Public URL: https://uwe-repository.worktribe.com/output/1079668
• Publisher URL: http://dx.doi.org/10.1080/0020772021000046225
• 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.