Delta wing rocking motion suppression by deep reinforcement learning

Abstract

The wing rock phenomenon is a self-excited roll motion occurring on the aircraft at high angles of attack (AoAs), which negatively impact safety and maneuverability. The limit cycle oscillation is a typical characteristic of this self-excited roll motion. A controller employing a model-free training approach is constructed on the basis of deep reinforcement learning (DRL) to address this severe nonlinear control problem. For an 80° swept delta wing model, the analytical model describing the nonlinear behavior of wing rock is presented and implemented in the simulation environment. The DRL-based controller is trained with the proximal policy optimization algorithm. A reward function is carefully designed for training to achieve excellent convergence stability. Various simulations were conducted at a series of unlearned initial conditions (roll angles, AoAs) to demonstrate the effectiveness and generalization capability of the proposed method and verify and evaluate the performance of the trained DRL controller. Finally, the scenario that undergoes disturbance is added to confirm the effectiveness and robustness of the proposed DRL-based controller. Results show that the DRL-based controller could effectively regulate the oscillation and retain the capability to suppress the un-wanted behavior with favorable generalization capability and robustness. Therefore, delta wing rocking motion suppression based on DRL has powerful intelligent control performance and provides a new idea for predicting the evolution of aerodynamics in rocking motion suppression.