In this paper, we present a methodology for constructing data-driven maneuver generation models for agile aircraft that can generalize across a wide range of trim conditions and aircraft model parameters. Maneuver generation models play a crucial role in the testing and evaluation of aircraft prototypes, providing insights into the maneuverability and agility of the aircraft. However, constructing the models typically requires extensive amounts of real pilot data, which can be time-consuming and costly to obtain. Moreover, models built with limited data often struggle to generalize beyond the specific flight conditions covered in the original dataset. To address these challenges, we propose a hybrid architecture that leverages a simulation model, referred to as the source model. This open-source agile aircraft simulator shares similar dynamics with the target aircraft and allows us to generate unlimited data for building a proxy maneuver generation model. We then fine-tune this model to the target aircraft using a limited amount of real pilot data. Our approach combines techniques from imitation learning, transfer learning, and reinforcement learning to achieve this objective. To validate our methodology, we utilize real agile pilot data provided by Turkish Aerospace Industries (TAI). By employing the F-16 as the source model, we demonstrate that it is possible to construct a maneuver generation model that generalizes across various trim conditions and aircraft parameters without requiring any additional real pilot data. Our results showcase the effectiveness of our approach in developing robust and adaptable models for agile aircraft.

提出了一种用于构建敏捷飞机的数据驱动机动生成模型的方法，该方法能够在广泛的修正条件和飞机模型参数范围内进行泛化。通过利用模拟模型和有限的真实飞行员数据，结合模仿学习、迁移学习和强化学习等技术，实现了对目标飞机的机动生成模型的优化和调整。验证为该方法在开发敏捷飞机的强大和适应性模型方面的有效性。

具有有限飞行员演示数据的鲁棒敏捷飞行器控制的集成模仿与强化学习方法