Parameters estimation of nonlinear models of DC motors using neural networks

I. F. El-Arabawy; H. A. Yousef; M. Z. Mostafa; H. M. Abdulkader

doi:10.1109/IECON.2000.972582

Parameters estimation of nonlinear models of DC motors using neural networks

I. F. El-Arabawy, H. A. Yousef, M. Z. Mostafa, H. M. Abdulkader

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

This paper considers the development of an estimation scheme for parameters of nonlinear models of dc motors using neural network. The neural network used in this paper is a linear recurrent neural network. This scheme is considered as an on line identification method based on minimization of the least square error between the actual and the estimated parameters. The stability and convergence of the proposed estimation scheme are presented. Numerical results show the effectiveness of the proposed scheme for parameters estimation of nonlinear model of a dc series motor.

Original language	English
Title of host publication	IECON Proceedings (Industrial Electronics Conference)
Publisher	IEEE Computer Society
Pages	1997-2000
Number of pages	4
Volume	1
DOIs	https://doi.org/10.1109/IECON.2000.972582
Publication status	Published - 2000

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

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AB - This paper considers the development of an estimation scheme for parameters of nonlinear models of dc motors using neural network. The neural network used in this paper is a linear recurrent neural network. This scheme is considered as an on line identification method based on minimization of the least square error between the actual and the estimated parameters. The stability and convergence of the proposed estimation scheme are presented. Numerical results show the effectiveness of the proposed scheme for parameters estimation of nonlinear model of a dc series motor.

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BT - IECON Proceedings (Industrial Electronics Conference)

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Parameters estimation of nonlinear models of DC motors using neural networks

Abstract

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