Comparison of SI-ANN and Extended Kalman Filter-Based Sensorless Speed Controls of a DC Motor

Abstract

In this study, a speed sensorless algorithm was developed to control a single-link manipulator connected to DC motor. The armature voltage value can be obtained by using duty cycle information generated by the controller without using any sensor. Thus, the proposed system does not require any additional measurement sensor. This paper presents a study for artificial neural network (ANN)-based speed estimation algorithm which has a closed-loop speed control with the first and second inputs generated via support of system identification (SI). The second SI input was obtained as a simple transfer function with discrete time. The performances of the SI-input ANN structure and the conventional extended Kalman filter (EKF) method were compared in the MATLAB/Simulink environment. It was observed that the proposed method revealed better results than the EKF method in the steady and transient states. Thus, it was shown that high-performance sensorless speed control could be performed with SI-ANN structure in applications.