Title :
Reduced neural observers for a class of MIMO discrete-time nonlinear system
Author :
Alanis, Alma Y. ; Sanchez, Edgar N. ; Hernandez, Esteban A.
Author_Institution :
Dept. de Cienc. Computacionales, Univ. de Guadalajara, Jalisco, Mexico
Abstract :
A nonlinear discrete-time reduced order neural observer for the state estimation of a discrete-time unknown nonlinear system, in presence of external and internal uncertainties is presented. The observer is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. This observer estimates the state of the unknown discrete-time nonlinear system, using a parallel configuration. The paper also includes the stability proof on the basis of the Lyapunov approach. To illustrate the applicability simulation results are included.
Keywords :
Kalman filters; Lyapunov methods; MIMO systems; discrete time systems; learning (artificial intelligence); nonlinear control systems; observers; recurrent neural nets; reduced order systems; uncertain systems; Lyapunov approach; MIMO system; discrete-time system; extended Kalman filter; nonlinear system; parallel configuration; recurrent high order neural network; reduced order neural observers; stability proof; state estimation; Control systems; Kalman filters; MIMO; Mathematical model; Neural networks; Nonlinear systems; Observers; Recurrent neural networks; State estimation; Uncertainty; Discrete-time nonlinear systems; HIV model; Kalman filtering learning; Recurrent high order neural networks; Reduced order neural observers;
Conference_Titel :
Electrical Engineering, Computing Science and Automatic Control,CCE,2009 6th International Conference on
Conference_Location :
Toluca
Print_ISBN :
978-1-4244-4688-9
Electronic_ISBN :
978-1-4244-4689-6
DOI :
10.1109/ICEEE.2009.5393313
