Stratospheric airships, as typical low-speed near space aircraft, exhibit great potential in the fields of communication relay and high-altitude monitoring. However, due to the considerable uncertainty of the external wind field environment, the deviation of stratospheric airship flight trajectory increases. To effectively predict the envelope of stratospheric airship flight trajectory, this paper proposes an innovative trajectory envelope prediction method to offer a new perspective on related research. By establishing wind field uncertainty model including both wind speed uncertainty and wind direction uncertainty, stratospheric airship trajectories are obtained under four extreme wind fields, and the trajectory envelope is generated using the Alpha-Shape method. Furthermore, the adaptive bandwidth kernel density estimation method is introduced to quantify the trajectory distribution probability, providing an intuitive description of the trajectory distribution probability. The simulation results show that the proposed methods can effectively predict the stratospheric airship flight trajectory envelope and describe trajectory distribution characteristics, supporting mission planning, risk assessment, and energy-management strategy selection for stratospheric airship flight tests.

Additional tilting mechanisms in tilt-rotor expand the control dimensions of the aircraft, thereby augmenting the complexity of the overall control system, particularly during the transition phase. This study examines the dynamic model, flight corridor for tilt operations, and trajectory planning relevant to a Distributed Compound Tiltrotor (DCT) and proposes a transition strategy for the transition corridor based on multi-objective constraints. In the modeling section, the stall effect of the tilt wing of the DCT under large tilt angles is considered. To overcome the limitation of traditional Static Tilt Corridors (STC), which fail to account for the dynamic constraints during the aircraft’s tilting process, a Multi-objective Constrained Tilt Corridor (MCTC) is proposed. This method is constrained by dynamic imbalance rules and power limitations, extending the reverse transition tilt angle to 120°, offering a more precise representation of the variation in the feasible domain during both forward and backward transitions of the DCT. Based on the extended MCTC, a transition trajectory planning strategy is developed by extracting the key features of the optimal trajectory. The design problem for the transition strategy is then formulated as a constrained optimal control problem. Simulation results demonstrate that this strategy maintains the transition trajectory within the middle of the MCTC and reduces the backward transition time by 5.2 s compared to traditional methods, improving both the robustness and safety of the transition. Finally, a trajectory tracking controller has been developed to evaluate the effectiveness of the transition strategy proposed in this paper.

Aiming at the station keeping control problem of stratospheric aerostat in dynamic wind field, a station keeping controller designed based on deep reinforcement learning D3QN algorithm for different control channels of aerostat operated with ambient wind, studied the impact of different reward functions on the performance of regional resident controllers. Station keeping control simulation was carried out under the task constraint of a station keeping duration of three days and a station keeping radius of 50 km. Results show that: compared with the station keeping controller designed by DDQN method, the performance of the controller designed by D3QN method is significantly improved. When the control trajectory of aerosat is only adjusted by altitude, the average station keeping radius can reach 25.26 km, and the station keeping ratio is 96.25%. With the aid of horizontal propulsion, the average station keeping radius can be significantly reduced and the station keeping time ratio can be significantly increased. At the same time, the strong robustness of the station keeping controller based on deep reinforcement learning was verified, and the controller can be designed with different reward functions to meet the requirements of different station keeping tasks.

Solar array is a key subsystem for stratospheric airships to achieve long-endurance station-keeping. A curved surface paving model for solar arrays was established, and a high-precision calculation method for the output performance of solar arrays was proposed. The effect patterns of multiple factors, including latitude, date, and heading, on the output performance of solar arrays were simulated and analyzed. Simulation results show that: in the summer of the northern hemisphere with favorable irradiation, the effect of latitude on the output performance of solar arrays is relatively small, while the effect is much greater in autumn and winter. Date has an important effect on the peak output power and total daily output power of solar arrays, especially in regions at middle and high latitudes. In low-latitude regions, the heading has little effect on the peak output power of the solar array, but a certain effect on the total daily output power of the solar array, while in high-latitude regions, the heading’s effect varies with the date, being smaller in the summer and larger in the winter. The findings can provide references for renewable energy system design and the overall design of stratospheric airships.