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<p>This research investigates the efficacy of advanced deep reinforcement learning algorithms in the field of unmanned aerial vehicle (UAV) attitude control. Deep reinforcement learning has demonstrated encouraging results in the robotics and high-level mission planning, its application to low-level mission planning remains understudied. There is a crucial need for more sophisticated control in UAV to operate in challenging and unpredictable environments. In this research study, we present a thorough assessment of the three groundbreaking deep reinforcement learning algorithms such as Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG) &amp; Trust Region Policy Optimization (TRPO) for UAV attitude control, quadcopters to be precise. We have utilized a high- fidelity simulation environment and experimentally assessed these algorithms’. ability to learn robust control policies. Evaluations based on rise time, peak response, error minimization and stability demonstrate that PPO outperforms DDPG and TRPO, achieving better precision and robustness. Our comparative results suggest that the controller with combination of adaptive gain optimization and DRL significantly improves the flight performance with an average of 19.9%, 6.6%, 16.5% improvement in rise time, peak percentage, error rate respectively and a complete stable flight.</p>

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Deep reinforcement learning for UAV attitude control via adaptive gain optimization

  • 期刊论文

  • 发表日期：

    2025年11月10日

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  • Muhammad Zorain,
  • Fawad Salam Khan,
  • Noman Hasany,
  • Zia Mohy Ud Din,
  • Jahan Zeb Gul

摘要

This research investigates the efficacy of advanced deep reinforcement learning algorithms in the field of unmanned aerial vehicle (UAV) attitude control. Deep reinforcement learning has demonstrated encouraging results in the robotics and high-level mission planning, its application to low-level mission planning remains understudied. There is a crucial need for more sophisticated control in UAV to operate in challenging and unpredictable environments. In this research study, we present a thorough assessment of the three groundbreaking deep reinforcement learning algorithms such as Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG) & Trust Region Policy Optimization (TRPO) for UAV attitude control, quadcopters to be precise. We have utilized a high- fidelity simulation environment and experimentally assessed these algorithms’. ability to learn robust control policies. Evaluations based on rise time, peak response, error minimization and stability demonstrate that PPO outperforms DDPG and TRPO, achieving better precision and robustness. Our comparative results suggest that the controller with combination of adaptive gain optimization and DRL significantly improves the flight performance with an average of 19.9%, 6.6%, 16.5% improvement in rise time, peak percentage, error rate respectively and a complete stable flight.