Deep Reinforcement Learning and Simultaneous Stabilization-Based Flight Controller for Nano Aerial Vehicle

Abstract

The plants of nano aerial vehicles (NAVs) are inherently unstable. Hence, a NAV needs a flight controller to accomplish a mission. Furthermore, the sensing and computational capabilities of NAV's autopilot hardware are limited. Hence, the implementation of the full state feedback controllers with gain scheduling is difficult. This paper proposes a flight controller scheme that consists of two parts: a Simultaneously Stabilizing Output Feedback Linear (SSOFL) controller and a Proximal Policy Optimization (PPO) deep reinforcement learning agent, which is connected in parallel to the SSOFL controller. In this scheme, the single SSOFL controller provides stabilization and nominal tracking performance to the NAV throughout its flight envelope by accomplishing simultaneous stabilization (SS). Additionally, the PPO agent is trained using the closed-loop (CL) nonlinear plant with this SSOFL controller to enhance the tracking performance. The effectiveness of the proposed flight controller scheme is verified using the six-degree-of-freedom nonlinear simulations of the fixed-wing nano aerial vehicle.