The stratospheric airship is a vital platform in near-space applications, and achieving autonomous transfer has become a key research focus to meet the demands of diverse mission scenarios. The core challenge lies in planning feasible and efficient paths, which is difficult for traditional algorithms due to the time-varying environment and the highly coupled multi-system dynamics of the airship. This study proposes a deep reinforcement learning algorithm, termed reward-prioritized Long Short-Term Memory Twin Delayed Deep Deterministic Policy Gradient (RPL-TD3). The method incorporates an LSTM network to effectively capture the influence of historical states on current decision-making, thereby improving performance in tasks with strong temporal dependencies. Furthermore, to address the slow convergence commonly seen in off-policy methods, a reward-prioritized experience replay mechanism is introduced. This mechanism stores and replays experiences in the form of sequential data chains, labels them with sequence-level rewards, and prioritizes high-value experiences during training to accelerate convergence. Comparative experiments with other algorithms indicate that, under the same computational resources, RPL-TD3 improves convergence speed by 62.5% compared to the baseline algorithm without the reward-prioritized experience replay mechanism. In both simulation and generalization experiments, the proposed method is capable of planning feasible paths under kinematic and energy constraints. Compared with peer algorithms, it achieves the shortest flight time while maintaining a relatively high level of average residual energy.

Tethered aerostats serve as critical platforms for aerial observation and communication; however, research on their lateral stability under varying wind conditions remains limited. In this study, a comprehensive approach combining dynamic modeling and computational fluid dynamics (CFD) simulations is employed to systematically analyze the lateral dynamic characteristics of an inverted Y-tail tethered aerostat. A nonlinear lateral dynamic model incorporating aerodynamic effects and added inertial terms is developed, and transient CFD simulations are conducted to examine the system’s dynamic responses under different wind directions and wind speeds. The study particularly evaluates the influence of key parameters such as payload position and the main tether point configuration. The results indicate that optimizing payload distribution can significantly enhance stability, manifested by reductions in yaw peaks and roll oscillation amplitudes. Furthermore, it is found that the yaw motion is more sensitive to wind-direction variations, while the roll motion is influenced by the coupled effects of both wind speed and direction. This research provides a valuable theoretical basis and a validated simulation framework for improving the structural design and operational stability of tethered aerostat systems in realistic wind environments.