Quadcopter neural controller for take-off and landing in windy environments

Consultable a partir de






Acceso embargado / Sarbidea bahitua dago
Artículo / Artikulua
Versión aceptada / Onetsi den bertsioa

Project identifier

Gobierno de Navarra//0011-1411-2021-000021
Gobierno de Navarra//0011-1365-2020-000078
Gobierno de Navarra//0011-1411-2021-000025


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.


Quadcopter, Take-off, Landing, Deep reinforcement learning, Wind, PPO, DDPG


Estadística, Informática y Matemáticas / Estatistika, Informatika eta Matematika / Institute of Smart Cities - ISC



Doctorate program

Editor version

Funding entities

This work has been supported in part by the Ministerio de Ciencia e Innovación (Spain) and European Union NextGenerationEU, Spain under the research grant TED2021-131716B-C21 SARA (Data processing by superresolution algorithms); in part by Agencia Estatal de Investigación (AEI), Spain and European Union NextGenerationEU/PRTR, Spain PLEC2021-007997: Holistic power lines predictive maintenance system; and in part by the Government of Navarre (Departamento de Desarrollo Económico), Spain under the research grants 0011-1411-2021-000021 EMERAL: Emergency UAVs for long range operations, 0011-1365-2020-000078 DIVA, and 0011-1411-2021-000025 MOSIC: Plataforma logística de largo alcance, eléctrica y conectada.

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