Artificial Neural Network Based DTC Driver for PMSM

This paper deals with Direct Torque Control(DTC) of Permanent Magnet Synchronous Motors(PMSM's) with using artificial neural networks. PMSM's are increasing application areas such as traction, electric and hybrid vehicles, aerospace and servo systems due to its well-known advantages such as high power density, lower mass and volume . This paper investigates solution for the chronically and the biggest problem of the DTC scheme on the PMSMs: high torque ripple. Otherwise, another main problem faced in the DTC scheme is difficulties due to complex algorithm to get high performance control of motors for industrial applications. For this purpose, two different artificial neural networks have been suggested replacing the optimal switching vector selection and flux sector determination process of conventional direct torque control scheme. Matlab/Simulink based numerical simulations have been carried out to compare the PMSM performances with conventional control scheme and proposed artificial neural network based scheme