Title :
A torque model study of switched reluctance motor using BP neural network
Author :
Haolai, Jia ; Yan, Chen ; Zhenmin, Wang
Author_Institution :
Taiyuan Univ. of Technol., China
Abstract :
In the paper, a neural network torque model of a switched reluctance motor (SRM) is established, based on the merits of the backpropagation (BP) neural network in the area of modeling and control of nonlinear systems. The simulation results show that the torque model based on BP-neural network is more robust and adaptive, and can reflect the working properties of SRM more accuracy than the local linearization torque model
Keywords :
adaptive control; backpropagation; control system analysis; control system synthesis; machine control; machine theory; neurocontrollers; nonlinear control systems; reluctance motors; robust control; torque control; SRM; backpropagation neural network; control design; control simulation; nonlinear system control; robust adaptive control; switched reluctance motor; torque model study; Control system synthesis; Feedforward neural networks; Neural networks; Neurons; Nonlinear control systems; Nonlinear systems; Reluctance machines; Reluctance motors; Robustness; Torque control;
Conference_Titel :
Electrical Machines and Systems, 2001. ICEMS 2001. Proceedings of the Fifth International Conference on
Conference_Location :
Shenyang
Print_ISBN :
7-5062-5115-9
DOI :
10.1109/ICEMS.2001.971854
