An Integrated Imitation and Reinforcement Learning Methodology for
  Robust Agile Aircraft Control with Limited Pilot Demonstration Data
Gulay Goktas Sever Umut Demir Abdullah Sadik Satir Mustafa Cagatay Sahin Nazim Kemal Ure
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.