Data-Driven Control Strategies for Rotary Wing Aerial Vehicles

To evaluate the performance of data-driven control methods applied to Unmanned Aerial Vehicles (UAVs), this work addresses the implementation of these strategies, particularly the Data-enabled Predictive Control (DeePC) algorithm. This strategy computes optimal controls for unknown systems through real-time output feedback using a receding horizon implementation. Moreover, this research investigates the influence of different hyperparameters on the DeePC’s performance and conducts a realistic comparison between this method and two model-based control approaches: Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC). The simulation results validate the applicability of DeePC algorithm and highlight its superior robustness to system degradation and yaw calibration errors. However, it is less suitable for complex nonlinear systems subject to aggressive trajectories.