Design of neural network controller for VRM inverter drive

Author

Ismail, F. ; Wahsh, S. ; Mohamed, A.

Author_Institution

Cairo Univ., Dokki, Egypt

fYear

1994

fDate

7-10 Aug 1994

Firstpage

185

Lastpage

191

Abstract

In this paper, the design of a dynamic neurocontroller with good robustness properties is presented for a variable reluctance motor (VRM) drive system. The goal of this controller is to determine the optimal excitation parameters that can be fed to the inverter which forces the drive system to operate at the required operating conditions of torque and speed. The neurocontroller is generated by a multilayer feedforward neural network which is trained with a back propagation algorithm

Keywords

backpropagation; control system synthesis; feedforward neural nets; machine control; multilayer perceptrons; neurocontrollers; power engineering computing; reluctance motor drives; robust control; back propagation algorithm; dynamic neurocontroller; multilayer feedforward neural network; neural network controller; neural network training; neurocontroller; optimal excitation parameters; robustness properties; variable reluctance motor drive; Control systems; Force control; Inverters; Multi-layer neural network; Neural networks; Neurocontrollers; Optimal control; Reluctance motors; Robustness; Torque control;

fLanguage

English

Publisher

ieee

Conference_Titel

Computers in Power Electronics, 1994., IEEE 4th Workshop on

Conference_Location

Trois-Rivieres, Que.

Print_ISBN

0-7803-2091-3

Type

conf

DOI

10.1109/CIPE.1994.396718

Filename

396718