Estimation of DC Motor Parameters Using Least Square-based Optimization Algorithm

In daily life, Direct Current (DC) motors are employed in virtually all applications due to their ease of operation, simple construction, and affordability. Therefore, a proper mathematical model for DC motors is essential for developing model-based controllers and predicting system responses. In this paper, the Least Square-based Autoregressive-eXogenous (LS-ARX) optimization algorithm has been developed to estimate unknown parameters of the DC motor model. Furthermore, the unknown parameters of the motor have been estimated by using the Nonlinear Least Square (NLS) and Pattern Search (PS) methods from the MATLAB optimization toolbox. For the purpose of estimating the DC motor parameters, an experimental setup has been constructed to measure the angular velocity of the motor utilizing an Arduino and two different sensors (photon interrupter and hall-effect sensors). To transfer data from the Arduino program to the MATLAB environment, a serial connection has been established between the Arduino and the Python program. Utilizing the proposed algorithm and the MATLAB optimization toolbox, the accuracy of the estimation process has been evaluated and compared through coefficient of determination. As a result of the comparison, it can be observed that the LS-ARX optimization algorithm is extremely robust, and the unknown parameters of the DC motor have been estimated with a high degree of accuracy.