Modelagem e controle de motor sem escovas utilizando filtro estendido de Kalman para estimação da velocidade e posição

Abstract

This work presents a mathematical modeling and computational simulation of the behavior of a brushless permanent magnet motor in conjunction with a three-phase inverter, using drive techniques, direct torque control and field-oriented control for testing the simulation and validation of the model, then the use of the Extended Kalman Filter is implemented to estimate the speed and position of the rotor in order to reduce the number of sensors needed to drive the motor in closed loop. An algorithm in python was obtained that models the motor and simulates its behavior during the activation by the FOC method using the speed and position estimated by the filter, the results were promising demonstrating that the system was able to follow the reference and correctly control the motor speed throughout the simulation.