Torque Ripple Minimization using an Artificial Neural Network based Speed Sensor less control of SVM-DTC fed PMSM Drive

Abstract

In this paper, an artificial neural network (ANN) controller based position sensorless control of permanent magnet synchronous motor (PMSM) using Space vector modulation-Direct torque control (SVM-DTC) for variable speed drive has been presented. The SVM-DTC require the initial position of the rotor during the starting of the PMSM drive. The installation of the shaft-mounted position sensor requires additional space, assembly, wiring circuit, and is fragile component. The speed sensor-less control of PMSM enhances the performance of drives in harsh environments and reduces the overall cost of the drive and improve mechanical reliability. The speed estimation requires the knowledge of drive parameters, the model-based speed control technique is suitable for low and medium-speed motor drive applications without knowing the exact parameter of the PMSM drive. The Rotor Flux based Model Reference adaptive system (RF-MRAS) is used for a wide speed operation and estimates rotor angle in dynamic conditions. The presence of an integrator in the voltage model of RF-MRAS affects the low speed performance of the drive, therefore to improve the speed response at low speed, the ANN controller is used to replace the Proportional-Integral (PI) controller, which is employed in the adaptive model of the speed observer. The performance of the control scheme is simulated at variable speed and load conditions with the help of the OPAL-RT 4500 simulation platform.