Modeling and Learning Control for Aircraft Trajectory Tracking in Continuous Climb and Descent Operations

The precise trajectory tracking is one of the most important topics in the climb and descent operations for aircraft in the dense terminal airspace. It facilitates the accurate execution of the planned trajectory, maintenance of safety separation, and improvement of airspace utilization. This paper aims to solve the aircraft trajectory tracking problem by proposing a class of modeling and learning-based control methods. A nonlinear, time-varying aircraft model is constructed under repetitive horizontal wind and non-repetitive turbulence, which is further converted into a linear form through a virtual input transformation. By resorting to the repetitive execution of trajectory tracking tasks in aircraft climb and descent operations, an adaptive iterative learning control (ILC) method is developed. Then, the ILC convergence analysis is constructed based on a composite energy function. As a result, precise trajectory tracking can be achieved under both repetitive and non-repetitive disturbances as the iteration number increases. A simulation based on the Continuous Climb Operation and Continuous Descent Operation procedures of Guangzhou Baiyun International Airport is conducted and the results verify the feasibility and efficacy of the proposed algorithm.