Title :
Self-tuning control of switched reluctance motors for optimized torque per ampere at all operating points
Author :
Fahimi, B. ; Suresh, G. ; Johnson, J.P. ; Ehsani, M. ; Arefeen, M. ; Panahi, I.
Author_Institution :
Dept. of Electr. Eng., Texas A&M Univ., College Station, TX, USA
Abstract :
Online self-tuning of control angles of a switched reluctance motor (SRM) is essential to optimize its performance in the presence of manufacturing imperfections. This paper reports an adaptive control scheme to optimize the torque per ampere at low and high speeds using artificial neural networks (ANN). An heuristic optimization technique has been introduced to find the changes in control angles. Using these results, the ANN will update its synaptic weights. Computer simulation has been employed to show the feasibility of this approach. Experimental results are provided to demonstrate the working of the self-tuning control
Keywords :
reluctance motors; adaptive control scheme; artificial neural networks; computer simulation; control angles; control design; control performance; control simulation; heuristic optimization technique; self-tuning control; switched reluctance motors; synaptic weights; Adaptive control; Artificial neural networks; Computer simulation; IEEE members; Inductance; Manufacturing; Programmable control; Reluctance machines; Reluctance motors; Torque control;
Conference_Titel :
Applied Power Electronics Conference and Exposition, 1998. APEC '98. Conference Proceedings 1998., Thirteenth Annual
Conference_Location :
Anaheim, CA
Print_ISBN :
0-7803-4340-9
DOI :
10.1109/APEC.1998.653986
