Deep reinforcement learning has demonstrated flexibility advantages in the control field of quadrotor aircraft. However, when there are sudden disturbances in the environment, especially special disturbances beyond experience, the algorithm often finds it difficult to maintain good control performance. Additionally, due to the randomness in the algorithm’s exploration of states, the model’s improvement efficiency during the training process is low and unstable. To address these issues, we propose a deep reinforcement learning framework guided by Multi-PID Self-Attention to tackle the challenges in the training speed and environmental adaptability of quadrotor aircraft control algorithms. In constructing the simulation experiment environment, we introduce multiple disturbance models to simulate complex situations in the real world. By combining the PID control strategy with deep reinforcement learning and utilizing the multi-head self-attention mechanism to optimize the state reward function in the simulation environment, this framework achieves an efficient and stable training process. This experiment aims to train a quadrotor simulation model to accurately fly to a predetermined position under various disturbance conditions and subsequently maintain a stable hovering state. The experimental results show that, compared with traditional deep reinforcement learning algorithms, this method achieves significant improvements in training efficiency and state exploration ability. At the same time, this study deeply analyzes the application effect of the algorithm in different complex environments, verifies its superior robustness and generalization ability in dealing with environmental disturbances, and provides a new solution for the intelligent control of quadrotor aircraft.

The increasing complexity of aerial acrobatics missions necessitates ever-higher levels of attitude control precision in fixed-wing unmanned aerial vehicles (UAVs). Traditional control methods, such as feedback linearization and small disturbance derivation linear models, falter in maintaining attitude tracking accuracy, due to the presence of unanticipated disturbances—most notably, wave disturbances during low-altitude maritime flights—and model uncertainties introduced by factors like large-angle maneuvers, intricate aerodynamic characteristics, and fuel consumption. Consequently, these limitations impede the successful execution of intricate maneuvers, such as looping, the split-S, the Immelmann turn, and the Pougatcheff cobra maneuver. In response to these challenges, we propose an integral sliding mode control based on disturbance observer (ISMC-DO) system to achieve robust attitude angle tracking amidst model uncertainties and mitigate the effects of wave disturbances. Additionally, quaternion representations are adopted as a supplement to Euler angles, thereby resolving the singularity issues inherent in the latter. By using the Lyapunov function, the ISMC-DO-based control system is shown to be asymptotically stable. Simulation results further validate that ISMC-DO can achieve high-precision attitude tracking control of the UAV under wave disturbance.