Robust-Neural Observer Design for Discrete-Time Uncertain Non-Affine Nonlinear System

Somayeh Rahimi (Department of Control Engineering, Science and Research branch, Islamic Azad University, Boroujed, Iran)
Saeed Mohammad-Hoseini (Department of Control Engineering, Science and Research branch, Islamic Azad University, Boroujed, Iran)

International Journal of Electrical and Computer Engineering
Vol 4, No 4: August 2014

This paper proposed a new Nonlinear Discrete-Time Robust-Neural Observer (DTRNO) which capable to give estimation for the states of Discrete-Time Uncertain Non-affine Non-linear Systems in presence of external disturbances. The Neural network is a kind of discrete-time Multi Layered Perceptron (MLP) which Trained with an Extended Kalman-Filter (EKF) based algorithm, which this neural observer is robust in presence of external and internal uncertainties, using a parallel configuration.This work includes the stability proof of the estimation error on the basis of the Lyapunov approach, and for demonstrate observer performance an Uncertain Non-affine Nonlinear Systems have been simulated to formulations validate the theoretical. Simulation results confirm the proficiency of the DTRNO even at the different operating conditions and presence of parameters uncertainties.DOI:http://dx.doi.org/10.11591/ijece.v4i4.6171