Intelligent Autopilot Design for Fixed-Wing UAVs: Robust Stabilization and Trajectory Tracking Using Advanced Evolutionary and Swarm Optimization Algorithms

This paper presents the development and analysis of an intelligent autopilot system for a fixedwing unmanned aerial vehicle (FWUAV), designed to stabilize and track critical flight parameters: airspeed, altitude, heading angle, and sideslip angle. The proposed system leverages a modular control architecture based on successive closed-loop control channels, enhanced by advanced optimization methods: Ant Lion Optimizer (ALO), Differential Evolution (DE), Bat Algorithm (BA), and Harmony Search (HS). Simulation results validate the robustness and efficiency of the autopilot system under various scenarios, including fixed set-point hovering and trajectory tracking for both straight-line and orbital paths, with and without wind perturbations. Comparative analyses with state-of-the-art methods from the literature demonstrate that the DE and ALO-based controllers consistently achieve superior performance in terms of precision and rapidity while maintaining adaptability to nonlinear dynamics and external disturbances. Furthermore, the findings highlight the potential of the proposed autopilot architecture and optimization methods in enhancing UAV control accuracy and robustness.