Taha Yacine Trad1,*, Kheireddine Choutri1, Mohand Lagha1, Souham Meshoul2, Fouad Khenfri3, Raouf Fareh4, Hadil Shaiba5
CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4757-4786, 2024, DOI:10.32604/cmc.2024.055634
- 19 December 2024
Abstract The popularity of quadrotor Unmanned Aerial Vehicles (UAVs) stems from their simple propulsion systems and structural design. However, their complex and nonlinear dynamic behavior presents a significant challenge for control, necessitating sophisticated algorithms to ensure stability and accuracy in flight. Various strategies have been explored by researchers and control engineers, with learning-based methods like reinforcement learning, deep learning, and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control systems. This paper investigates a Reinforcement Learning (RL) approach for both high and low-level quadrotor control systems, focusing on attitude stabilization and position… More >
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