Title :
DTC of PMSM based on artificial neural networks with regulation speed using the fuzzy logic controller
Author :
Essalmi, Adil ; Mahmoudi, Hassan ; Abbou, Ahmed ; Bennassar, Abderrahim ; Zahraoui, Yassine
Author_Institution :
Dept. of Electr. Eng., Mohammed V Univ., Agdal Rabat, Morocco
Abstract :
This paper presents an improved direct torque control of Permanent magnetic synchronous motor (PMSM) based on neural network (NN) and fuzzy logic (FL) technique. The major problem that is usually associated with direct torque control (DTC) drive is the high torque ripple. This paper proposes to replace the conventional selector switches statements of the voltage inverter by a selector based on artificial neural networks (ANN) and replace the classic integral proportional (IP) controller by fuzzy controller (FC) of speed in order to reduce torque ripple and increase the response time period of the system. The validity of the proposed methods is confirmed by the simulation results using Matlab/Simulink.
Keywords :
PI control; fuzzy control; invertors; machine control; neural nets; permanent magnet motors; synchronous motors; torque control; velocity control; DTC; IP controller; Matlab; PMSM; Simulink; artificial neural networks; direct torque control; fuzzy logic controller; fuzzy logic technique; high torque ripple; integral proportional controller; permanent magnetic synchronous motor; regulation speed; response time period; selector switches statement; voltage inverter; Artificial neural networks; Backpropagation; Gold; Niobium; Software packages; Vectors; direct torque control; fuzzy logic; neural network; permanent magnetic synchronous motor;
Conference_Titel :
Renewable and Sustainable Energy Conference (IRSEC), 2014 International
Print_ISBN :
978-1-4799-7335-4
DOI :
10.1109/IRSEC.2014.7059801