Real-time recurrent neural state estimation.
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.
Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Jalisco 44430, Mexico. almayalanis@gmail.com
Alma Y
AY
Alanis
Edgar N
EN
Sanchez
Alexander G
AG
Loukianov
Marco A
MA
Perez
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation / Q000592:standards; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013663:Teaching; D013997:Time Factors