Data-driven Trajectory Tracking Control for Nonlinear Low-altitude Vehicles

This paper addresses the trajectory tracking control problem for nonlinear low-altitude vehicles by proposing a data-driven, model-free adaptive control scheme to circumvent the complexities and challenges associated with traditional modeling. To reduce tracking errors for unmanned aerial vehicles (UAVs), a parameter dynamic updating mechanism is introduced. Based on data-driven techniques and parameter updates, a novel adaptive flight control algorithm is presented. Then, simulation examples are provided to demonstrate the effectiveness and stability of the proposed algorithm by incorporating three types of communication interruptions and compensations to analyze their impacts on the system. Finally, parameter tuning is conducted through PID comparison experiments.