Torque ripple minimization for switched reluctance motor with predictive current control method

2017 ◽  
Author(s):  
Cai Hui ◽  
Mengqiu Li ◽  
Wang Hui ◽  
Shi Qi Shen ◽  
Wenbin Wang
Keyword(s):  
Control Method ◽  
Switched Reluctance Motor ◽  
Torque Ripple ◽  
Current Control ◽  
Switched Reluctance ◽  
Reluctance Motor

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.

scholarly journals A simplified hysteresis current control for cascaded converter fed switched reluctance motor

2019 ◽  
Vol 9 (6) ◽  
pp. 5095
Author(s):  
Anuradha Devi Tellapati ◽  
Malligunta Kiran Kumar
Keyword(s):  
Control Method ◽  
Switched Reluctance Motor ◽  
Industrial Applications ◽  
Current Control ◽  
Switched Reluctance ◽  
Hysteresis Current Control ◽  
Switching Losses ◽  
Cascaded Converter ◽  
Reluctance Motor ◽  
Power Switches

<p>Simple constructional features with no windings on rotor circuit and robustness make switched reluctance motor (SRM) a most used motors in industrial applications. Peak motor voltage rating depends on the rated voltage of the power switches. In conventional asymmetrical converter driving SRM, voltage rating of the motor depends on rating of power electronic switches in converter. Demand to rise the motor rating insists to put pressure on converter switching components which results in increased switching losses. A cascaded converter topology for SRM reduces the rating of switching components as compared to conventional converters for SRM. This paper presents a cascaded converter fed SRM drive with reduced switching losses. The paper presents a simplified hysteresis current control (HCC) for cascaded converter fed SRM. Simplified HCC control method reduces switching losses as HCC is applied to only one bridge of cascaded converter. Though the performance of the SRM remains same with cascaded converter fed SRM with HCC applied to only one bridge or to two bridges and with conventional asymmetrical converter, the switching losses are reduced to a great extent when HCC applied to one bridge of cascaded converter fed SRM. Performance of SRM is illustrated with conventional asymmetrical converter fed SRM and is compared to cascaded converter while HCC applied to only one bridge and applied to two bridges of cascaded converter. Proposed work is simulated using MATLAB/SIMULINK and results are presented.</p>

Fuzzy Neural Network Control for Robot Manipulator Directly Driven by Switched Reluctance Motor

2012 ◽  
pp. 387-398
Author(s):  
Baoming Ge ◽  
Aníbal T. de Almeida
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Robot Manipulator ◽  
Switched Reluctance Motor ◽  
Torque Ripple ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Fuzzy Neural

Applications of switched reluctance motor (SRM) to direct drive robot are increasingly popular because of its valuable advantages. However, the greatest potential defect is its torque ripple owing to the significant nonlinearities. In this paper, a fuzzy neural network (FNN) is applied to control the SRM torque at the goal of the torque-ripple minimization. The desired current provided by FNN model compensates the nonlinearities and uncertainties of SRM. On the basis of FNN-based current closed-loop system, the trajectory tracking controller is designed by using the dynamic model of the manipulator, where the torque control method cancels the nonlinearities and cross-coupling terms. A single link robot manipulator directly driven by a four-phase 8/6-pole SRM operates in a sinusoidal trajectory tracking rotation. The simulated results verify the proposed control method and a fast convergence that the robot manipulator follows the desired trajectory in a 0.9-s time interval.

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