Title :
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
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;
Conference_Titel :
Electric Machines and Drives Conference Record, 1997. IEEE International
Conference_Location :
Milwaukee, WI
Print_ISBN :
0-7803-3946-0
DOI :
10.1109/IEMDC.1997.604206
