Title :
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
fDate :
3/1/2011 12:00:00 AM
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;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2010.2103322
