Performance of a Four Phase Switched Reluctance Motor Speed Control Based On an Adaptive Fuzzy System: Experimental Tests,Analysis and Conclusions

2006 ◽  
pp. 581-599 ◽  
Author(s):  
Silviano Rafael ◽  
A.J. Pires ◽  
P.J. Costa Branco
Keyword(s):  
Fuzzy System ◽  
Speed Control ◽  
Experimental Tests ◽  
Switched Reluctance Motor ◽  
Motor Speed ◽  
Switched Reluctance ◽  
Adaptive Fuzzy ◽  
Reluctance Motor ◽  
Adaptive Fuzzy System

Design of Switched Reluctance Motor Speed Control System Base on DSP

2013 ◽  
Vol 462-463 ◽  
pp. 727-734
Author(s):  
Xiao Li ◽  
Chao Hui Zhang ◽  
Ying Jie Fan
Keyword(s):  
Control System ◽  
High Efficiency ◽  
Low Cost ◽  
Speed Control ◽  
Switched Reluctance Motor ◽  
Power Converter ◽  
Motor Speed ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
Speed Control System

Switched Reluctance Motor has become one of hot topics of the international transmission for its simple structure,wide speed range and low cost, etc. In this thesis, based on TI's 32-bit DSP (TMS320F240) and following the principles of structure matching, high efficiency, easy to control, switched reluctance motor speed control system is designed using a minimum program of the main switching device. The parts of design include power converter, controller and software components.

scholarly journals Adaptive neuro-fuzzy controller of switched reluctance motor

2007 ◽  
Vol 4 (1) ◽  
pp. 23-34 ◽  
Author(s):  
Ahmed Tahour ◽  
Hamza Abid ◽  
Ghani Aissaoui
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Expert Knowledge ◽  
Digital Simulation ◽  
Switched Reluctance Motor ◽  
Motor Speed ◽  
Inference System ◽  
Switched Reluctance ◽  
Neuro Fuzzy ◽  
Reluctance Motor

This paper presents an application of adaptive neuro-fuzzy (ANFIS) control for switched reluctance motor (SRM) speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI).

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