Optimal Position Control of a DC Motor Using LQG with EKF

This paper deals with the implementation of the Linear Quadratic Gaussian (LQG) with an Extended Kalman Filter (EKF) for the position control of a PMDC motor. LQG is a popularly used linear optimal control technique in literature. However, the direct implementation of LQG with the use of the Kalman Filter as the optimal estimator is incapable of adapting to changes in the system parameters which results in a deviation from the expected optimal performance. EKF allows for the estimation of the system parameter values along with the unknown states of the system. The estimated values are used to constantly update the plant model and calculate the gains for optimal performance. The effectiveness of this technique on the DC motor position control system for various changes in system parameter values is studied in this paper and compared with the performance of a simple Kalman Filter to show the improvement in performance. The results show improvements in both step responses and tracking performances with the use of the EKF estimator along with the LQG controller