The Applied Research of Rotor Position Sensorless Detection of Switched Reluctance Motor Based on Genetic RBF Neural Network

2010 ◽  
Author(s):  
Jun Hang ◽  
You-rui Huang ◽  
Lei Shen
Keyword(s):  
Neural Network ◽  
Applied Research ◽  
Rbf Neural Network ◽  
Switched Reluctance Motor ◽  
Switched Reluctance ◽  
Rotor Position ◽  
Reluctance Motor ◽  
Position Sensorless

scholarly journals Model Free Adaptive Control of Switched Reluctance Motor for Electric Vehicle

2021 ◽  
pp. 104-114
Author(s):  
Xifeng Mi , Yuanyuan Fan
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Control Method ◽  
Rbf Neural Network ◽  
Switched Reluctance Motor ◽  
Torque Ripple ◽  
Torque Distribution ◽  
Switched Reluctance ◽  
Model Free ◽  
Reluctance Motor

In this paper, the model free adaptive control method of switched reluctance motor for electric vehicle is studied. Based on the torque distribution control of SRM, a SRM control strategy based on torque current hybrid model based on RBF neural network is proposed in this paper. Based on the deviation between the dynamic average value and instantaneous value of SRM output torque, the online learning of RBF neural network is realized. At the same time, this paper constructs a torque current hybrid model, obtains the current variation law of SRM under low torque ripple operation, and reduces the torque ripple of SRM. The SRM torque distribution control is realized on the SRM experimental platform. Compared with the voltage chopper control method, the experimental results show that the torque ripple of SRM can be reduced by adopting the torque distribution control strategy.

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.

Torque Ripple Suppression of Switched Reluctance Motor Based on Torque Sharing Strategy

2014 ◽  
Vol 960-961 ◽  
pp. 1086-1090 ◽  
Author(s):  
Qian Zhang ◽  
Ying Zhao ◽  
Hao Mu ◽  
Shuai Liu ◽  
Yi Heng Li
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Rbf Neural Network ◽  
Switched Reluctance Motor ◽  
Torque Ripple ◽  
Current Compensation ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Sharing Strategy ◽  
Main Disadvantage

The torque ripple is the main disadvantage of switched reluctance motor (SRM). In order to reduce the torque ripple of SRM, and improve the performance of the system, the torque sharing strategy was combined with RBF neural network for the purpose of torque ripple suppression by controlling the winding current of each phase. In consideration of the possible error of network, the real-time current compensation was taken to compensate the loss of torque which could suppress the torque ripple of the system in further. The results show that he torque ripple of SRM was suppressed effectively.

Sign in / Sign up

Export Citation Format

Share Document