Switched reluctance motor control with artificial neural networks

Author

Garside, Jeffrey J. ; Brown, Ronald H. ; Arkadan, Abd A.

Author_Institution

Dept. of Electr. & Comput. Eng., Marquette Univ., Milwaukee, WI, USA

fYear

1997

Abstract

This paper presents a new control scheme for switched reluctance motor drives based on artificial neural networks (ANN). The ANNs are trained to generate drive circuitry phase current references for velocity reference tracking. A new, application specific ANN architecture is used to improve modeling accuracy. The control ANNs are trained using data from a state space model. The control scheme characteristics are then presented via two case studies. Firstly, a constant velocity control is simulated and a comparison with previously measured results is presented. A velocity reference tracking case study is then presented

Keywords

control system analysis; control system synthesis; learning (artificial intelligence); machine control; machine theory; neurocontrollers; reluctance motor drives; state-space methods; application specific ANN architecture; artificial neural networks; constant velocity control; control design; control scheme characteristics; control simulation; drive circuitry phase current references; modeling accuracy; state-space model training data; switched reluctance motor drives; velocity reference tracking; Artificial neural networks; Circuits; Friction; Nonlinear equations; Reluctance machines; Reluctance motors; Rotors; State-space methods; Torque; Velocity control;

fLanguage

English

Publisher

ieee

Conference_Titel

Electric Machines and Drives Conference Record, 1997. IEEE International

Conference_Location

Milwaukee, WI

Print_ISBN

0-7803-3946-0

Type

conf

DOI

10.1109/IEMDC.1997.604206

Filename

604206