scholarly journals Genetic algorithm design of neural network and fuzzy logic controllers

2000 ◽  
Vol 4 (3) ◽  
pp. 186-192 ◽  
Author(s):  
A. Hunter ◽  
K.-S. Chiu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Algorithm Design ◽  
Fuzzy Logic Controllers ◽  
Logic Controllers

scholarly journals ANFIS controller for vector control of three phase induction motor

2020 ◽  
Vol 19 (3) ◽  
pp. 1177
Author(s):  
Girisha Joshi ◽  
Pinto Pius A J
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Vector Control ◽  
Induction Motor ◽  
Control Technique ◽  
Fuzzy Logic Controllers ◽  
Inference System ◽  
Artificial Neural ◽  
Logic Controllers

For variable speed drive applications such as electric vehicles, 3 phase induction motor is used and is controlled by fuzzy logic controllers. For the steady functioning of the vehicle drive, it is essential to generate required torque and speed during starting, coasting, free running, braking and reverse operating regions. The drive performance under these transient conditions are studied and presented. In the present paper, vector control technique is implemented using three fuzzy logic controllers. Separate Fuzzy logic controllers are used to control the direct axis current, quadrature axis current and speed of the motor. In this paper performance of the indirect vector controller containing artificial neural network based fuzzy logic (ANFIS) based control system is studied and compared with regular fuzzy logic system, which is developed without using artificial neural network. Data required to model the artificial neural network based fuzzy inference system is obtained from the PI controlled induction motor system. Results obtained in MATLAB-SIMULINK simulation shows that the ANFIS controller is superior compared to controller which is implemented only using fuzzy logic, under all dynamic conditions.

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