Performance Evaluation of Evolutionary and Swarm Algorithms for UAV Attitude Control

Unmanned Aerial Vehicles (UAVs) require precise control over their pitch and roll angles to maintain stability and maneuverability. Traditional PID controllers are often used for this purpose; however, manual tuning of PID parameters can be time-consuming and suboptimal. This paper presents a comparative study of four optimization techniques; Grey Wolf Optimizer (GWO), Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for automatic tuning of PID parameters in the pitch/roll control system of a UAV. To improve modeling clarity and reproducibility, the complete PID control law and the resulting closed-loop transfer function are explicitly provided. Simulation results demonstrate the efficacy of each technique in improving system performance, measured by overshoot, settling time, and mean absolute error. Based on the Overshoot, Peak Time, and Rise Time, both PSO and GA performed better in terms of faster and more stable system responses.