Sensorless Position Control of Switched Reluctance Motors Based On Artificial Neural Networks

Author

Enayati, Babak ; Saghaiannejad, S.M.

Author_Institution

Dept. of Electr. & Comput. Eng., Isfahan Univ. of Technol.

Volume

3

fYear

2006

fDate

9-13 July 2006

Firstpage

2266

Lastpage

2271

Abstract

This paper presents a new artificial neural network method for position control of switched reluctance motors, in this method the flux observer is not needed and the data sources used for training are voltages and currents of two phases, so the designed neural network has four inputs and one output which is the rotor position. The advantage of this method is its low torque ripple in comparison with shaft encoder observers and also because of not having any flux observer, any uncertainties of phase resistance does not affect training process. In this paper the development and operation of an ANN-based position estimator for a three-phase SRM is presented. The experimental process has been implemented on a 6/4, 4 kW SRM

Keywords

machine control; magnetic flux; neurocontrollers; observers; position control; reluctance motors; shafts; artificial neural networks; flux observer; low torque ripple; phase resistance; position estimator; rotor position; sensorless position control; shaft encoder observers; switched reluctance motors; three-phase SRM; Artificial neural networks; Inductance; Neural networks; Position control; Reluctance machines; Reluctance motors; Rotors; Stators; Torque; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics, 2006 IEEE International Symposium on

Conference_Location

Montreal, Que.

Print_ISBN

1-4244-0496-7

Electronic_ISBN

1-4244-0497-5

Type

conf

DOI

10.1109/ISIE.2006.295925

Filename

4078600