An Adaptive Iterative Learning Control Method for Continuous Climb Operation and Continuous Descent Operation

The promotion and implementation of trajectory-based operation concept in civil aviation industry arise a higher requirement for trajectory tracking precision, especially in continuous climb and descent operations. Considering the repetitive external disturbances like wind and measurement error, an adaptive iterative learning control method is introduced to improve the performance of trajectory steering. A six degree-of-freedom aircraft plant model combining attitude and position control is constructed to support more accurate 4D trajectory tracking. Based on the similarity of target trajectory and disturbance suffered by sequential flights, an iterative learning scheme is customized, which exploits recurrent disturbance information contained in prior flight data to modify input signal and compensate for deviation. Specifically, a feedback linearization term is used to overcome the nonlinearity of aircraft model and an adaptive gain strategy is designed to suppress nonrepetitive noise signal. Simulations based on BADA database are conducted, whose results validate the feasibility and tracking precision of the proposed method.