Error-Tracking Based Iterative Learning Control for Quadrotor UAV

In this paper, an error-tracking based iterative learning control (ILC) trajectory tracking strategy is proposed for quadrotor unmanned aerial vehicle (UAV) systems with random initial state shift. A decay-gain-based desired error trajectory is constructed to relax the initial condition constraints, permitting arbitrary system initialization. Furthermore, a decay-gain function is introduced to effectively regulate the convergence rate of the desired error trajectory and suppress the maximum overshoot. Utilizing Lyapunov-based stability analysis, an iterative learning controller with fully saturated update law is developed to ensure boundedness and asymptotic convergence of the quadrotor UAV tracking errors. Numerical simulations are provided to verify the effectiveness of the developed strategy.