Generation and Simulation of Basic Maneuver Action Library for 6-DOF Aircraft by Reinforcement Learning

Abstract

The development of modern air combat requires aircraft to have certain intelligent decision-making ability. In some of the existing solutions, the automatic control of aircraft is mostly composed of the upper mission decision and the lower control system. Although the underlying PID (Proportional Integral Derivative) based controller has a good performance in stable conditions, it lacks stability in complex environments. So, we need to design a new system for the problem of aircraft decision making. Studies have shown that the behavior of an aircraft can be viewed as a combination of several basic maneuvers. The establishment of aircraft basic motion library will effectively reduce the difficulty of upper aircraft control. Given the good performance of reinforcement learning to solve the problem with continuous action space, in this paper, reinforcement learning is used to control the aircraft's rod and rudder to generate a basic maneuver action library, and the flight of the aircraft under the 6 degrees of freedom (6-DOF) simulation engine is effectively controlled. The simulation results verify the feasibility of the method on a visual simulation platform.