Implementation of Kalman Filter on Pid Control System for DC Motor Under Noisy Condition

Abstract

DC motors are actuators that are widely used in various fields. The reason is that DC motors are easy to control, high torque at low speed, and fast response. Angular velocity of DC motor is regulated automatically by using certain controls method, the most commonly used of which is PID control. The performance of the control system decreases in the presence of disturbance or noise. The presence of noise give has negative impacts such as instability in control response, decreased accuracy, and difficulty in tuning PID gain. The most common disturbance comes from the inaccuracy data due to measurement noise and process noise. In this study, the Kalman filter is proposed as a state estimator to reduce the influence of noise, both process noise and measurement noise. The Kalman filter provides an optimal estimate of the angular velocity of DC motor by minimizing the mean squared error. The estimated angular velocity from Kalman Filter is utilized as input for PID control. Simulation results show that the Kalman filter is capable to reduces the influence of measurement noise. In nominal condition, PID control give an Integral Absolute Error (IAE) of 344.56. Under noisy condition, PID control (without Kalman filter) has an IAE of 517.27, while Kalman filter-based PID control has an IAE of 345.25. The IAE reduction of 99.6% indicates that the proposed control system effectively minimizes errors, resulting in better performance and stability.