A deep reinforcement learning control approach for high-performance aircraft

Abstract

This research introduces a flight controller for a high-performance aircraft, able to follow randomly generated sequences of waypoints, at varying altitudes, in various types of scenarios. The study assumes a publicly available six-degree-of-freedom (6-DoF) rigid aeroplane flight dynamics model of a military fighter jet. Consolidated results in artificial intelligence and deep reinforcement learning (DRL) research are used to demonstrate the capability to make certain manoeuvres AI-based fully automatic for a high-fidelity nonlinear model of a fixed-wing aircraft. This work investigates the use of a deep deterministic policy gradient (DDPG) controller agent, based on the successful applications of the same approach to other domains. In the particular application to flight control presented here, the effort has been focused on the design of a suitable reward function used to train the agent to achieve some given navigation tasks. The trained controller is successful on highly coupled manoeuvres, including rapid sequences of turns, at both low and high flight Mach numbers, in simulations reproducing a prey–chaser dogfight scenario. Robustness to sensor noise, atmospheric disturbances, different initial flight conditions and varying reference signal shapes is also demonstrated.

Off‐policy reinforcement learning algorithm for robust optimal control of uncertain nonlinear systems

“…Its purpose is to facilitate coordination among active loads, enabling them to collectively react to any alterations in the load conditions. A novel approach is introduced in Reference 27, which utilizes deep reinforcement learning (RL) to design a control system for F‐16 aircraft. The primary goal of this scheme is to accomplish precise target tracking while ensuring optimal performance.…”