Application of associative memory neural networks to the control of a switched reluctance motor

Author

Reay, D.S. ; Green, T.C. ; Williams, B.W.

Author_Institution

Dept. of Comput. & Electr. Eng., Heriot-Watt Univ., Edinburgh, UK

fYear

1993

fDate

15-19 Nov 1993

Firstpage

200

Abstract

The application of an associative memory neural network to the problem of torque ripple minimisation in a switched reluctance motor is presented. Conventional techniques for torque linearisation and decoupling are reviewed, after which the application of neural techniques to the problem is described. An instrumented test rig based around a 4 kW IGBT converter and a four phase switched reluctance motor has been constructed. Results obtained experimentally and by simulation demonstrate the effectiveness of the approach. The neural network has been implemented using both digital signal processor and field programmable gate array technologies

Keywords

content-addressable storage; linearisation techniques; machine control; neural nets; reluctance motors; torque control; associative memory neural networks; digital signal processor; field programmable gate array; instrumented test rig; switched reluctance motor; torque decoupling; torque linearisation; torque ripple minimisation; Associative memory; Digital signal processors; Field programmable gate arrays; Instruments; Insulated gate bipolar transistors; Neural networks; Reluctance motors; Switching converters; Testing; Torque;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics, Control, and Instrumentation, 1993. Proceedings of the IECON '93., International Conference on

Conference_Location

Maui, HI

Print_ISBN

0-7803-0891-3

Type

conf

DOI

10.1109/IECON.1993.339081

Filename

339081