Real-Time Recurrent Neural State Estimation

Author

Alanis, Alma Y. ; Sanchez, Edgar N. ; Loukianov, Alexander G. ; Perez, Marco A.

Author_Institution

Centro Univ. de Cienc. Exactas e Ingenierias, Univ. de Guadalajara, Guadalajara, Mexico

Volume

22

Issue

3

fYear

2011

fDate

3/1/2011 12:00:00 AM

Firstpage

497

Lastpage

505

Abstract

A nonlinear discrete-time neural observer for discrete-time unknown nonlinear systems in presence of external disturbances and parameter uncertainties is presented. It is based on a discrete-time recurrent high-order neural network trained with an extended Kalman-filter based algorithm. This brief includes the stability proof based on the Lyapunov approach. The applicability of the proposed scheme is illustrated by real-time implementation for a three phase induction motor.

Keywords

Kalman filters; neural nets; nonlinear control systems; observers; real-time systems; state estimation; Kalman filter based algorithm; discrete time neural observer; external disturbances; nonlinear systems; parameter uncertainties; real-time recurrent neural state estimation; Artificial neural networks; Induction motors; Mathematical model; Nonlinear systems; Observers; Discrete-time nonlinear systems; extended Kalman filtering; neural state estimation; real-time implementation; recurrent neural networks; Algorithms; Artificial Intelligence; Computer Simulation; Neural Networks (Computer); Nonlinear Dynamics; Teaching; Time Factors;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2010.2103322

Filename

5688461