Extended Kalman Filter Estimator and Mamdani Fuzzy Logic Controller for Sensorless DC Motor Speed Control

Abstract

This article proposes a sensorless speed control method for DC motors that employs an extended Kalman filter (EKF) estimator and a Mamdani fuzzy logic controller (FLC). In many industries, expensive measurement systems are typically necessary for effective control and monitoring. However, this sensorless approach offers a cost-effective alternative that also enhances system reliability and robustness. The EKF estimates motor speed based solely on the armature current. Concurrently, the FLC assists in mitigating motor parameter variations and load torque nonlinearity in closed-loop speed control for various speed references. A comparative analysis of the performance of the EKF-based FLC with an FLC-based PI controller reveals that the former surpasses the latter in terms of time-domain specifications and absolute error performance indices. The integration of sensorless speed control techniques in DC motors has garnered significant attention due to its cost effectiveness and efficiency. This paper explores the use of an Extended Kalman Filter (EKF) estimator in conjunction with a Fuzzy Logic Controller (FLC) to improve the speed control performance of DC motors. The proposed system is implemented in MATLAB/Simulink, and a prototype model of a sensorless speed-controlled DC motor has been developed to validate this innovative technique.