Reinforcement Learning-based Intelligent Flight Control for a Fixed-wing Aircraft to Cross an Obstacle Wall

This paper develops an intelligent flight controller for a fixed-wing aircraft model in the longitudinal plane, using a Reinforcement Learning (RL)-based control method, namely Deep Deterministic Policy Gradient (DDPG). The neural net-work controller is fed the values of aircraft position, velocity, pitch angle and pitch rate, and outputs the elevator deflection. Artificial Neural Network (ANN)s are used to approximate the nonlinear state-action value function and the policy function. Simulation results show that the flight controller learns from the experienced data to fly over an obstacle wall with constrained pitch angle.