DEEP SPACE ANTENNA POSITION CONTROL USING OPTIMIZED PID WITH ARTIFICIAL INTELLIGENCE

Abstract

This thesis presents the design, optimization, and comparative analysis of advanced control strategies for a two-axis (azimuth–elevation) antenna tracking system. The main objective is to improve tracking accuracy, dynamic response, and robustness under realistic operating conditions. To achieve this, several control approaches are investigated, including classical Proportional–Integral–Derivative (PID) controllers, Sliding Mode Control (SMC), and a hybrid SMC-PID scheme. In order to enhance controller performance, metaheuristic optimization algorithms, namely Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Ant Colony Optimization (ACO), are employed to tune the controller parameters. The performance evaluation is conducted using the Integral of Time-weighted Absolute Error (ITAE) as the primary objective function, ensuring both fast transient response and minimal steady-state error. The simulation results demonstrate that classical tuning methods such as Ziegler–Nichols provide limited performance due to oscillations and slower convergence. In contrast, optimization-based PID controllers significantly improve system behavior, with ACOPID achieving the best performance among standalone PID approaches. Furthermore, the integration of SMC with PID in a hybrid structure leads to superior results, combining robustness against disturbances with smooth tracking performance. The proposed ACO-SMC-PID controller exhibits the best overall performance, characterized by reduced tracking error, fast convergence, improved stability, and efficient control effort. The findings confirm that hybrid nonlinear control strategies optimized using metaheuristic algorithms provide an effective and reliable solution for high-precision antenna tracking applications.