Title :
Parameters estimation of nonlinear models of DC motors using neural networks
Author :
El-Arabawy, I.F. ; Yousef, H.A. ; Mostafa, M.Z. ; Abdulkader, H.M.
Author_Institution :
Fac. of Eng., Alexandria Univ., Egypt
Abstract :
This paper considers the development of an estimation scheme for parameters of nonlinear models of DC motors using neural networks. The neural network used in this paper is a linear recurrent neural network. This scheme is considered as an online 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
Keywords :
DC motors; control system analysis; least squares approximations; machine control; machine theory; neural nets; parameter estimation; DC motors; control simulation; convergence; least square error minimization; linear recurrent neural network; nonlinear models parameter estimation; online identification method; stability; Buildings; DC motors; Least squares approximation; Multi-layer neural network; Neural networks; Nonlinear dynamical systems; Parameter estimation; Power system modeling; Recurrent neural networks; Velocity control;
Conference_Titel :
Industrial Electronics Society, 2000. IECON 2000. 26th Annual Confjerence of the IEEE
Conference_Location :
Nagoya
Print_ISBN :
0-7803-6456-2
DOI :
10.1109/IECON.2000.972582
