Adaptive iterative learning control for impact load cyclic fatigue simulator of aircraft

The aircrafts have many structural components that withstand repeated impact loads, which may accumulate fatigue and potentially cause major accidents. To simulate repeated impact loads, it is imperative to design an impact load cyclic fatigue simulator that applies repeated impact loads to structural components, such as landing gears. Furthermore, the impact load simulator must simulate various loads, and the identical set of parameters employed in conventional controllers are challenging to apply to varying operational conditions. Consequently, the controller must possess learning and adaptive capabilities. Based on the characteristics of repeated impact loads, an Adaptive Iterative Learning Control (AILC) based on the backstepping method is developed in this study. This AILC comprises backstepping control law, parameter adaptation law, iterative learning law, and robust dynamical control term. The adaptation law is not only utilized to estimate unknown system parameters, but also for online identification of system parameters. The iterative learning law can be utilized to learn the characteristics of the system under repeated operating conditions. The robust dynamical control term ensures the stability of the entire system. The experimental results indicate that the AILC can achieve tracking error convergence within a finite time and effectively achieve high-precision torque command tracking.