Enhanced PSO-Tuned Fractional-Order Adaptive Model Predictive Control for Robust UAV-Quadrotor Trajectory Tracking

This paper presents an innovative approach to UAV-Quadrotor (UAV-Q) trajectory tracking through an Enhanced Particle Swarm Optimization (ePSO)-Tuned Fractional-Order Adaptive Model Predictive Control (FOAMPC). The proposed methodology leverages fractional calculus to capture the inherent memory and hereditary properties of the UAV-Q dynamics, enhancing the system’s responsiveness and stability. An ePSO algorithm dynamically tunes the controller gain in real-time, incorporating adaptive inertia weights and a convergence criterion based on particle position variance to prevent premature convergence and ensure a robust balance between exploration and exploitation. The performance index minimizes the tracking error and control effort over a prediction horizon, subject to system dynamics and constraints. By integrating this novel adaptive tuning mechanism, the controller adapts to varying conditions and disturbances, achieving enhanced trajectory tracking performance and robustness. The effectiveness of the proposed FOAMPC in handling complex UAV-Q maneuvers and disturbances is demonstrated through both simulation and real-world experiments, significantly outperforming conventional control strategies. This work provides a comprehensive framework for advanced UAV-Q control, paving the way for enhanced operational reliability and precision in dynamic environments.