Energy-Optimal Flight Strategy for Solar-Powered Aircraft Using Reinforcement Learning With Discrete Actions

The low efficiency of photovoltaic cells limits the energy absorption of high-altitude long-endurance (HALE) solar-powered unmanned aircraft vehicles (UAVs), which dramatically weakens the capacity for long-endurance missions. Therefore, finding a method to extend the flight duration with finite solar energy drives extensive research. The present work introduces a method that applies a deep reinforcement learning (DRL) framework to generate an energy-optimized flight strategy for HALE solar-powered aircraft. The neural network controller is designed to realize autonomous flight navigation by giving commands of thrust, attack angle, and bank angle. A mission area with a radius of 5 km is assumed to test the RL controller performance. The simulation results show that the RL controller leads to a 28 % increase in the battery SoC after a 24-hour flight, which indicates that a controller based on the RL framework might be a potential method for solving the solar-powered UAV trajectory planning problem. Aiming to explore the applicability of the RL controller, a sustained flight test is implemented. The results show that a 39-day endurance flight is achieved by the RL controller, which is 50% higher than the base case with a steady flight trajectory.

Automated summary

This study introduces a new energy-optimized flight strategy using a deep reinforcement learning framework to extend the flight time of high-altitude solar-powered UAVs while achieving autonomous flight navigation. Simulation results and a sustained flight test demonstrate that the controller based on the reinforcement learning framework has potential for solving the solar-powered UAV trajectory planning problem.