Cascade MFAC-based data-driven control for aircraft in 4D trajectory tracking

Abstract

This study aims to explore a trajectory tracking control approach in four-dimensional (4D) trajectory operation by employing cascade model-free adaptive control (MFAC) technique. The primary objective is to tackle the complexities associated with modeling aircraft mechanisms in the context of tracking control. By utilizing measurable input and output data, an equivalent dynamic linearized data model of the aircraft is constructed. Subsequently, position and speed controllers are developed based on MIMO-CFDL-MFAC and SISO-CFDL-MFAC, respectively. These controllers are then integrated to form an active-follower control system. Finally, a numerical simulation was conducted to track the entire 4D trajectory, encompassing climb, cruise, and descent stages, using historical data from flight CX8176 as a reference trajectory. Unlike previous researches, the proposed method eliminates the requirement for an aircraft mechanism model in the controller design, and it is verified using real flight data as a reference trajectory. The findings demonstrate that the cascade MFAC method can effectively minimize trajectory tracking errors, while also offering advantages in terms of energy conservation and emission reduction.