Speed control of an induction motor using the fuzzy logic approach

Ouiguini, R.; Djeffal, K.; Oussedik, A.; Megartsi, R.

doi:10.1109/isie.1997.648906

Cited by 5 publications

(1 citation statement)

References 2 publications

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“…For that a fuzzy controller is developed .Input applied to fuzzy controller is the error (e r ) and change in error (Pe r ) and the output obtained is the error that is corrected by the controller based on the rules provided in it [8]. Defuzzification method provided is centroid [10]; they can able to provide more accurate result than any other defuzzification method [9].And which is given in equation (3), The block diagram point out that the controlled output from the fuzzy controller is converted to equivalent three phase current and which is the provided to one of the input of the pulse generator. Another input is provided with the actual current from the motor.…”

Section: A Fuzzy Controlled Induction Motormentioning

confidence: 99%

Comparison on the performance of Induction motor control using fuzzy and ANFIS controllers

Simon

Geetha

2013

2013 IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN)

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This paper presents the fuzzy and ANFIS control system for Induction motor drives for better performance. The design and simulation of fuzzy logic controller and ANFIS for Induction motor are carried out based on fuzzy set theory and Back propagation. Fuzzy Controller will produce the output based on the rules provided and that are based on human experience. Whereas ANFIS is a best tradeoff between neural and fuzzy system which provide smoothness, due to the fuzzy controller (FC) interpolation and adaptability due to the neural network (NN) Back propagation. Simulated result for Fuzzy and ANFIS controlled Induction motor shows that latter exhibit better results.

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Section: A Fuzzy Controlled Induction Motormentioning

confidence: 99%