A Landing Trajectory Tracking Controller for Fixed-Wing UAV Based on Iterative Learning Control

Landing is the most critical flight phase of a fixed-wing Unmanned Aerial Vehicle (UAV). An accurate trajectory tracking during the landing phase determines whether a UAV can be safely recovered, while the non-linear trajectory in the flare phase makes it difficult to be tracked accurately. Consider that the trajectory in the flare phase is usually pre-defined in many cases, in this paper, we propose a more practical way that use Iterative Learning Control (ILC) to gradually determine the proper input to the UAV by repeating the landing maneuver. The tracking error of each attempt is stored, processed, and becomes the compensation for the next flight. To minimize the risk, these attempts can be performed in mid-air until the tracking error meets the requirements. In this paper, an analysis based on the nonlinear aircraft dynamic model shows that the tracking error caused by model inaccuracy cannot be compensated by the integrator. Then, An ILC based landing trajectory tracking controller is designed. Simulation results demonstrate the effectiveness of the proposed ILC controller.