Design and comparison of particle swarm optimization tuned Kalman filter based linear quadratic Gaussian controller and linear quadratic regulator for surface to air missile guidance system

The study of missile guidance systems is a well-known nonlinear control engineering area of research. To enhance the control performance of a missle guidance system, several technologies have been proposed in existing works. To resolve the weighting matrix selection issue of a linear quadratic Gaussian (LQG) controller for the surface-to-air missile guidance control system, this study utilizes the particle swarm optimization (PSO) technique. Selecting the best state (Q) and input (R) weighting matrices is a significant difficulty in the design of the LQG controller for real-time applications since it affects the controller's performance and optimality. The weighting matrices are often chosen by a trial-and-error method that not only complicates the design but also does not yield optimal outcomes. Therefore, in this paper, a PSO method is developed and used in the design of the linear quadratic regulator (LQR) and LQG controllers for the surface-to-air missile control system to choose the elements of the Q and R matrices in the best possible way. Finally, a comparative analysis between the designed controllers was presented. The results shows that a good performance was achieved by using the proposed PSO-tuned design process. The LQG and LQR are designed by manually adjusting the weighting matrices and utilizing an intelligent procedure, PSO algorithm which achieved optimal results. Further results indicate that the designed controllers, the PSO tuned LQR and LQG achieved a better performance over the manually adjusted LQR and LQG controllers.