The stratospheric airship, as a near-space vehicle, is increasingly utilized in scientific exploration and Earth observation due to its long endurance and regional observation capabilities. However, due to the complex characteristics of the stratospheric wind field environment, trajectory planning for stratospheric airships is a significant challenge. Unlike lower atmospheric levels, the stratosphere presents a wind field characterized by significant variability in wind speed and direction, which can drastically affect the stability of the airship’s trajectory. Recent advances in deep reinforcement learning (DRL) have presented promising avenues for trajectory planning. DRL algorithms have demonstrated the ability to learn complex control strategies autonomously by interacting with the environment. In particular, the proximal policy optimization (PPO) algorithm has shown effectiveness in continuous control tasks and is well suited to the non-linear, high-dimensional problem of trajectory planning in dynamic environments. This paper proposes a trajectory planning method for stratospheric airships based on the PPO algorithm. The primary contributions of this paper include establishing a continuous action space model for stratospheric airship motion; enabling more precise control and adjustments across a broader range of actions; integrating time-varying wind field data into the reinforcement learning environment; enhancing the policy network’s adaptability and generalization to various environmental conditions; and enabling the algorithm to automatically adjust and optimize flight paths in real time using wind speed information, reducing the need for human intervention. Experimental results show that, within its wind resistance capability, the airship can achieve long-duration regional station-keeping, with a maximum station-keeping time ratio (STR) of up to 0.997.

System architecture design is crucial for forward design in aerostat system engineering, yet a comprehensive research framework has been lacking. This paper presents a new method for stratospheric airship architecture preliminary design and optimization trade-off, grounded in Model-Based Systems Engineering (MBSE) theory. Firstly, a requirement analysis for a stratospheric airship is conducted using SysML, leading to the analysis and acquisition of the airship’s mission architecture design. Additionally, a multidisciplinary coupling simulation platform is developed with MATLAB, and the architecture preliminary design’s Pareto front is derived using the NSGA-II algorithm. Finally, based on the Pareto optimization set, the TOPSIS algorithm is applied to derive the optimal architecture preliminary design scheme for the airship. The optimization results validate the accuracy of the architecture preliminary design obtained from the requirement analysis, the reliability of the multidisciplinary coupling simulation platform, and the feasibility of the optimization algorithms. This comprehensive study spans the requirement analysis to the optimal architecture scheme, providing theoretical reference and design guidance for the forward design of airship systems engineering.

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.

Path planning for stratospheric airships in dynamic wind fields is challenging due to complex wind variations and strict nighttime energy constraints. This paper proposes a hybrid Level-Set Particle Swarm Optimization (LS-PSO) framework. Firstly, it employs PSO to search iteratively for a propulsion velocity sequence in the velocity domain, with a multi-objective fitness function that integrates reachability, energy consumption and time cost to evaluate each velocity sequence. Then, the reachability of each candidate sequence is numerically solved by the Level Set forward evolution. To improve optimization efficiency, we proposed a multi-resolution grid adaptive strategy for forward evolutions. Finally, with the optimal velocity sequence, the optimal path is generated once by the Level Set backtrack processing. To validate the resulting methodology, we used a benchmark case of a dynamic complex four-gyre flow, described by mathematical formulas, with the optimal day–night path identified by GPOPS-II. The results show the LS-PSO solution has comparable accuracy, with a trajectory deviation less than 3%. Then, we tested the methodology in the stratospheric wind flows using ERA5 reanalysis data. The results demonstrate that our path planning methodology provides a computationally efficient and optimal energy–time solution for autonomous stratospheric airships, while conforming to reachability and strict nighttime energy constraints.

A fan is part of the core equipment of a super-pressure balloon altitude control system, and high-performance fans have a significant impact on the altitude control capability and flight safety of super-pressure balloons. This paper proposes a mixed-flow MIX-140 fan for use with super-pressure balloons. Changes in the fan’s operating characteristics at various flight altitudes of a super-pressure balloon were investigated. First, the performance of the fan at ground level was obtained through numerical simulation and compared with measured data of the prototype to verify the accuracy of the simulation analysis. On this basis, the influences of changes in the atmospheric pressure, temperature, and fan speed on fan performance were investigated through numerical simulation. Furthermore, the MIX-140 fan was compared with an existing fan, and the variation of two parameters, namely, the ratio of inflation volume per unit time and the ratio of inflation volume per unit power, were investigated at different altitudes. Finally, the changes in the operating characteristics of the fan under different high-altitude environments were investigated through actual testing. The results reveal that changes in altitude can lead to significant changes in fan performance, and changes in the atmospheric pressure, temperature, and fan speed affect the fan’s working characteristics. Compared with the existing fan, the MIX-140 fan achieves an average increase of 295.8% in the inflation volume per unit of time, and 14.6% in the inflation volume per unit of power at altitudes of 16–20 km. The performance variation characteristics and testing methods of this proposed super-pressure balloon fan can provide a foundation and reference for the design of a super-pressure balloon control system.