PARTICLE SWARM OPTIMIZATION OF A KALMAN FILTER FOR SPEED ESTIMATION OF DC MOTOR

Abstract

DC motors have many critical applications in our daily lives. They can be used in many fields, including electric vehicles and household appliances. Therefore, the demand for DC motor control, particularly in terms of speed, has increased over the years. To control DC motors, it is necessary to measure relevant parameters such as motor speed and armature current using sensors. However, the installation of sensors will increase the system's cost, and it is challenging for DC motors due to space and weight limitations. To overcome this problem, it is necessary to design a measurement system with fewer sensors. A new method for estimating DC motor speed without using speed sensors is described in this paper, utilizing the Kalman filter for motor speed estimation due to its resistance to external disturbances and its ability to predict states and parameters. The Kalman filter (KF) requires tuning for improved estimation, which can be a time-consuming and laborious process. For that reason, the swarm optimization algorithm is used to optimize the Kalman filter. This paper will present the application of a swarm optimization algorithm (PSO) in optimizing the Kalman filter to estimate the speed of a DC motor through simulation in MATLAB and Simulink. The simulation results have shown the effectiveness of the proposed method