Title :
High efficiency drives for synchronous reluctance motors using neural network
Author :
Senjyu, Tomonobu ; Shingaki, Takeshi ; Omoda, Akihiro ; Uezato, Katsumi
Author_Institution :
Ryukyus Univ., Okinawa, Japan
Abstract :
A high efficiency drive technique for the synchronous reluctance motors (SRMs) using a neural network (NN) is presented in this paper. Since the NN can map the nonlinear relation, the high efficiency SRM drive does not require an accurate machine model. Moreover, the proposed method has robustness against machine parameter variations because the NN is learned on-line in this paper. The usefulness of the proposed method is verified by experiments
Keywords :
control system synthesis; learning (artificial intelligence); machine control; machine testing; machine theory; neurocontrollers; reluctance motor drives; machine parameter variations; neural network; nonlinear relation mapping; online learning; robustness; synchronous reluctance motor drives; AC motors; DC motors; Electronic mail; Equivalent circuits; Fuzzy control; Iron; Neural networks; Reluctance motors; Robustness; Velocity control;
Conference_Titel :
Industrial Electronics Society, 2000. IECON 2000. 26th Annual Confjerence of the IEEE
Conference_Location :
Nagoya
Print_ISBN :
0-7803-6456-2
DOI :
10.1109/IECON.2000.972221
