四轴飞行器
解耦(概率)
人工神经网络
计算机科学
强化学习
适应(眼睛)
控制理论(社会学)
控制器(灌溉)
过程(计算)
人工智能
控制工程
控制(管理)
工程类
航空航天工程
农学
物理
光学
生物
操作系统
作者
Xabier Olaz,Daniel Aláez,Manuel Prieto,J. Villadangos,José Javier Astráin
标识
DOI:10.1016/j.eswa.2023.120146
摘要
This paper proposes the design of a quadcopter neural controller based on Reinforcement Learning (RL) for controlling the complete maneuvers of landing and take-off, even in variable windy conditions. To facilitate RL training, a wind model is designed, and two RL algorithms, Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO), are adapted and compared. The first phases of the learning process consider extended exploration states as a warm-up, and a novel neural network controller architecture is proposed with the addition of an adaptation layer. The neural network’s output is defined as the forces and momentum desired for the UAV, and the adaptation layer transforms forces and momentum into motor velocities. By decoupling attitude from motor velocities, the adaptation layer enhances a more straightforward interpretation of the neural network output and helps refine the rewards. The successful neural controller training has been tested up to 36 km/h wind speed.
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