Contemporary autopilot systems for unmanned aerial vehicles (UAVs) are far more limited in
their flight envelope as compared to experienced human pilots, thereby restricting the
conditions UAVs can operate in and the types of missions they can accomplish
autonomously. This paper proposes a deep reinforcement learning (DRL) controller to
handle the nonlinear attitude control problem, enabling extended flight envelopes for fixed-
wing UAVs. A proof-of-concept controller using the proximal policy optimization (PPO) …

The fixed-wing UAV is a non-linear and strongly coupled system. Controlling UAV attitude
stability is the basis for ensuring flight safety and performing tasks successfully. The non-
linear characteristic of the UAV is the main reason for the difficulty of attitude stabilization.
Deep reinforcement learning for the UAV attitude control is a new method to design
controller. The algorithm learns the nonlinear characteristics of the system from the training
data. Due to the good performance, the PPO algorithm is the mainly algorithm of …