High-Performance Torque Control for Switched Reluctance Motor Based on Online Fuzzy Neural Network Modeling

2010 ◽  
Author(s):  
Xuelian Yao ◽  
Ruiyun Qi ◽  
Zhiquan Deng ◽  
Jun Cai
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Fuzzy Neural Network ◽  
Network Modeling ◽  
Switched Reluctance Motor ◽  
Torque Control ◽  
Neural Network Modeling ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Fuzzy Neural

Modeling of Switched Reluctance Motor Based on Dynamic Fuzzy Neural Network

2012 ◽  
Vol 220-223 ◽  
pp. 665-668 ◽  
Author(s):  
Ai De Xu ◽  
Shan Shan Zhang ◽  
Di Sun
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Switched Reluctance Motor ◽  
Mathematic Model ◽  
Modeling Method ◽  
System Structure ◽  
Actual System ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Fuzzy Neural

This paper proposed a novel mathematic model for switched reluctance motor(SRM):dynamic fuzzy neural network(D-FNN) was used to model for SRM based on the inductance characteristics, namely experimentally measured sample data. Compared with other modeling method, the inductance based on D-FNN can be trained on line and has the advantages of compact system structure and strong generalization ability. The SRM system is simulated with the trained inductance model. Compared with the actual system, the current waves are similar. This proves the new modeling method is correct and feasible.

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.

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

2011 ◽  
Vol 5 (3) ◽  
pp. 86-98
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.

scholarly journals Predictive Direct Torque Control of Switched Reluctance Motor for Electric Vehicles Drives

2020 ◽  
Author(s):  
Reyad Abdelfadil ◽  
László Számel
Keyword(s):  
Electric Vehicles ◽  
High Performance ◽  
Direct Torque Control ◽  
Switched Reluctance Motor ◽  
Power Converter ◽  
Torque Control ◽  
Switching Frequency ◽  
Suggested Approach ◽  
Switched Reluctance ◽  
Reluctance Motor

The electrical drive systems utilized in Electric Vehicles (EVs) applications must be reliable and high performance. To providing these specifications, it is essential to design high-efficiency electric motors and develop high-performance controllers. This study introduces direct torque control of Switched Reluctance Motor (SRM) for electric vehicle applications using Model Predictive Control (MPC) technique. The direct torque control using MPC is proposed to maintain the motor torque and motor speed to tracking desired signals with a satisfactory response. In this study, the MPC algorithm was programmed in C- language, and the simulation tests were performed using a non-linear model of 6/4 - 60 kW SRM that is fed with the symmetrical converter. The proposed controller was tested under different load conditions to verify the robustness of the controller, as well as at variable speeds to investigate the tracking performance. Thanks to the proposed method, the SRM torque ripples, stator copper losses, and average switching frequency of the power converter can reduce effectively due to applying a cost function that combines multiple objectives. The obtained outcomes show the effectiveness of the suggested approach compared to conventional direct torque control techniques.

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