Reinforcement Learning Control for a 2-DOF Flight Attitude Simulator

This paper focuses on the control problem for a two-degree-of-freedom flight simulator experimental setup, proposing a reinforcement learning-based flight attitude controller. The flight simulator aims to simulate the aircraft attitude control system, requiring consideration of its nonlinearity, model uncertainty, and the impact of external disturbances when designing the controller. Proximal Policy Optimization (PPO), as a policy gradient-based deep reinforcement learning algorithm, autonomously learns an approximately optimal controller based on a given objective function without the need for a mathematical model of the controlled object. Thanks to the application of the Actor-Critic framework and neural networks, the training of the two-degree-of-freedom flight simulator controller can rapidly converge within a short period. Simulations validate the generalization capability of the trained PPO controller and its robustness to external disturbances.