Trajectory-based aircraft operation and control is one of the hot issues in air traffic management. However, the accurate mechanism modeling of aircraft is tough work, and the operation data have not been effectively utilized in many studies. So, in this work, we apply the model-free adaptive iterative learning control method to address the time-of-arrival control problem in trajectory-based aircraft operation. This problem is first formulated into a trajectory tracking problem with along-track wind disturbance. Through rigorous analysis, it is shown that this method, combined with point-to-point iterative learning control (ILC) strategy, can effectively deal with the arrival time control problem with multiple time constraints. Then, the terminal ILC strategy is applied, aiming to resolve the same problem with a time constraint at the end point. Compared with the PID (Proportional Integral Derivative) type ILC, the proposed method improves control performance by 11.15% in root mean square of tracking error and 9.32% in integral time absolute error. The sensitivity and flexibility of the data-driven approach is further verified through numerical simulations.

中文翻译：

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控制到达时间和沿航迹风效应的基于轨迹的飞机操作的数据驱动方法

基于轨迹的飞机运行和控制是空中交通管理中的热点问题之一。然而，飞机的精确机构建模是一项艰巨的工作，许多研究并未有效利用运行数据。因此，在这项工作中，我们应用无模型自适应迭代学习控制方法来解决基于轨迹的飞机操作中的到达时间控制问题。该问题首先被公式化为具有沿轨道风扰动的轨迹跟踪问题。通过严谨的分析表明，该方法结合点对点迭代学习控制（ILC）策略，可以有效处理多时间约束的到达时间控制问题。然后，应用终端 ILC 策略，旨在解决相同的问题，但在终点有时间限制。与PID(Proportional Integral Derivative)型ILC相比，该方法的跟踪误差均方根误差提高了11.15%，积分时间绝对误差提高了9.32%。通过数值模拟进一步验证了数据驱动方法的敏感性和灵活性。