Event-Triggered Iterative Learning for Constrained UAV Attitude Control

This paper addresses the attitude control of quadrotor unmanned aerial vehicles (UAVs) with state constraints. We propose an event-triggered iterative learning control (ILC) scheme for quadrotor UAVs. The scheme effectively handles both parameter and non-parameter uncertainties in the UAV system through iterative learning laws. Additionally, an event-triggering mechanism is incorporated into the ILC design to reduce the update frequency of the final control signals, thereby reducing the signal transmission burden and control costs of the system. A barrier Lyapunov function is adopted to design the attitude control of the UAVs with state constraints. The convergence of the tracking error is analyzed using a constructed barrier composite energy function, ensuring that the prescribed constraints are not violated at each iteration. Simulation results demonstrate that the proposed control scheme achieves accurate tracking performance and effectively alleviates the system burden, thereby verifying its effectiveness and feasibility.