Nonlinear Modeling of Switched Reluctance Motor Based on BP Neural Network

2007 ◽  
Author(s):  
Yan Cai ◽  
Chao Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Nonlinear Modeling ◽  
Switched Reluctance Motor ◽  
Switched Reluctance ◽  
Reluctance Motor

Position Sensorless Control System of SRM Based on Improved BP Neural Network

2012 ◽  
Vol 468-471 ◽  
pp. 2187-2192 ◽  
Author(s):  
Li Xiao ◽  
He Xu Sun ◽  
Feng Gao
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Computing Time ◽  
Switched Reluctance Motor ◽  
Detection Accuracy ◽  
Training Time ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Position Sensorless

Due to the shortcomings of long training time and slow convergence of BP neural network, this paper presents a new improved method that weight is no longer a constant but turned into a function of adjustable parameters. After the training of the improved BP neural network is completed, the network can map the nonlinear relationship between motor current, flux and rotor position. Based on the analysis of the unique structural properties of switched reluctance motor, this paper also proposes a method of greatly reducing the sample data to save computing time. Simulation results show that this method simplifies the complexity of the control system and improve detection accuracy, thus realize position sensorless detection of the switched reluctance motor.

scholarly journals Intelligent Speed Regulation Control in Switched Reluctance Motor of Electric Vehicle Based on Neural Network Parameter Identification

2021 ◽  
pp. 173-180
Author(s):  
Huang Zongjian
Keyword(s):  
Neural Network ◽  
Parameter Identification ◽  
Electric Vehicle ◽  
Bp Neural Network ◽  
Switched Reluctance Motor ◽  
Network Parameter ◽  
Nonlinear Simulation ◽  
Switched Reluctance ◽  
Speed Regulation ◽  
Reluctance Motor

This paper studies the intelligent speed regulation control of switched reluctance motor of electric vehicle based on neural network parameter identification. Starting with the analysis of the performance of switched reluctance motor, the nonlinear flux linkage characteristic inversion model and torque characteristic model of switched reluctance motor are established based on BP neural network. This paper studies and improves the fast self configuration algorithm of BP neural network. Finally, the nonlinear simulation model of switched reluctance motor is established under Matlab/Simulink. The model can be used for further control research. In this paper, the integrated control method of instantaneous torque control based on torque observation and three-step commutation control is studied, and the simulation analysis is carried out. The results show that this method can effectively reduce the torque ripple of switched reluctance motor and improve the performance of its drive system.

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