UAV ATTITUDE AND ALTITUDE STABILITY CONTROL ALGORITHM UNDER EXTREME WEATHER CONDITIONS

Extreme weather conditions pose significant challenges to the stability of attitude and altitude control of uncrewed aerial vehicles (UAVs). Traditional control methods often have problems with response lag and reduced accuracy in intense disturbance environments. This paper proposes a hybrid control framework that integrates disturbance observers and deep reinforcement learning strategies to improve the autonomous control capabilities of UAVs under complex meteorological disturbances. This method models the disturbance trend in real time by extending the state observer. It uses the policy network to dynamically adjust the control output according to the disturbance estimation, thus realizing the closed-loop optimization of perception making. In simulation experiments, the proposed method shows excellent control performance under multiple typical disturbance conditions such as crosswind, gusts, downdrafts, and their combinations. Compared with traditional PID, LQR, and MPC controllers, it significantly improves trajectory stability, control accuracy, and energy consumption. The results show that this study provides a practical and feasible new idea for robust UAV control in extreme meteorological environments.