Modelling and control of bearingless switched reluctance motor based on artificial neural network

2005 ◽  
Author(s):  
Jianbo Sun ◽  
Qionghua Zhan ◽  
Liming Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Switched Reluctance Motor ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Artificial Neural ◽  
And Control

scholarly journals Artificial neural network vector controlled common high-side switch asymmetric converter fed switched reluctance motor drive

2021 ◽  
Vol 24 (2) ◽  
pp. 697
Author(s):  
Ashok Kumar Kolluru ◽  
Malligunta Kiran Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Machine ◽  
Switched Reluctance Motor ◽  
Switched Reluctance ◽  
Reluctance Machine ◽  
Reluctance Motor ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Cost

<p>The best alternative machine for synchronous and induction machine is switched reluctance machine for various applications. An artificial neural network (ANN) based vector controller is implemented for novel converter to drive switched reluctance motor (SRM) in this paper. To reduce the cost and simplified the controller an effective configuration of converter is proposed with only 4 pulse-withmodulation (PWM) based switches. The 6 pole stator and 4 pole rotor machine is considered in this paper to present results based on MATLAB. The ripples in torque are reduced by proposing vector controller by using novel configuration of converter. Generally SRM machines are having high ripples in torque, hence less number of switches will be feasible solution to drive the machine in order to reduce ripples. The proposed controller can also help to operate system with less ripples in torque since the controller having both torque and flux hysteresis controllers. The extensive results are presented on Simulink platform to validate the proposed method under both steady state as well as transient conditions.</p>

scholarly journals Artificial Neural Network-Based Control of Switched Reluctance Motor for Torque Ripple Reduction

2020 ◽  
Vol 2020 ◽  
pp. 1-31
Author(s):  
Iqra Tariq ◽  
Raheel Muzzammel ◽  
Umar Alqasmi ◽  
Ali Raza
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Switched Reluctance Motor ◽  
Torque Ripple ◽  
Switched Reluctance Motors ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Artificial Neural ◽  
Torque Ripples

Switched reluctance motor is acquiring major attention because of its simple design, economic development, and reduced dependability. These attributes make switched reluctance motors superior to other variable speed machines. The major challenge associated with the development of a switched reluctance motor is its high torque ripple. Torque ripple produces noise and vibration, resulting in degradation of its performance. Various techniques are developed to cope with torque ripples. Practically, there exists not a single mature technique for the minimization of torque ripples in switched reluctance motors. In this research, a switched reluctance motor is modelled and analysed. Its speed and current control are implemented through artificial neural networks. Artificial neural network is found to be a promising technique as compared with other techniques because of its accuracy, reduced complexity, stability, and generalization. The Levenberg–Marquardt algorithm is utilized in artificial neural networks due to its fast and stable convergence for training and testing. It is found from research that artificial neural network-based improved control shows better performance of the switched reluctance motor. Realization of this technique is further validated from its mean square error analysis. Operating parameters of the switched reluctance motor are improved significantly. Simulation environment is created in Matlab/Simulink.

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