Estimated Parameters in DC Motor Using Prediction Error Minimization Method

Abstract

This article presents a methodology to estimate, with a minimum of data and applying the algorithm of SteiglitzMcbride, the parameters characterizing the electrical part and mechanical part of a permanent magnet DC motor, with the aim of generating a model that represents its behavior. First, we review the state of the art of the subject. Then we explain why we chose an AXR model to represent the motor, and we show some definitions concerning the SteiglitzMcbride algorithm. Further, we develop an analytical modeling of a DC motor and simplify it in order to apply the algorithm. As example, we used the measured values in a permanent magnet DC motor to estimate the related parameters. Then, we simulated the operation of the motor with these estimated parameters and we compare the results obtained with the measured values.