The conversion efficiency of solar energy and the capacity of energy storage batteries are the key technologies limiting the development of solar-powered aircraft. In this paper, a mission-oriented design for the 3-dimensional (3D) path planning of solar-powered unmanned aerial vehicles (SP-UAVs) using limited solar energy to maximize the mission effectiveness is presented. Based on the solar radiation received model, the energy model, and the kinetic and kinematic model of the SP-UAV, a task planning problem oriented to multiobjective optimization is proposed. Both the pseudospectral and colony algorithms are proposed to search for the optimal mission path, and their joint optimization is employed to realize continuous flight and improve the flight mission capabilities. Explicitly, a multiobjective joint strategy is developed, including maximum power ascending, maximum range flight, maximum glide endurance, and minimum power level flight at night. Numerical and simulation results indicate that our proposed design outperforms the existing approaches not only in solar energy utilization but also in universality.

One of the key technologies for achieving day and night flight, tracking solar peak, and reducing flight energy consumption for a near-space solar-powered unmanned aerial vehicle (UAV) is trajectory planning. However, the environmental differences faced by the near-space solar-powered UAV during long-term flight pose challenges to its online trajectory planning. This article introduces a hierarchical guidance method designed using a hierarchical reinforcement learning algorithm, which includes a two-layer neural network structure of bottom-level trajectory planning models and a top-level decision model. The top-level decision maker selects the appropriate bottom-level planner based on flight and current environmental information, while the planner outputs thrust, attack angle, and tilt angle commands based on the input information. This hierarchical guidance structure can improve the UAV’s adaptability to energy environment variations and realize an autonomous flight based on energy maximization in long-term missions. Flight simulations spanning spring, summer and autumn seasons show that the guidance controller is able to switch flight policies on its own as the environment changes, allowing the UAV to maximise energy gain on each day, thereby achieving the best energy management strategy in long-term flight. The simulation results also verify the over-fitting and under-fitting effects of the neural network in the solar UAV trajectory planning task, providing support for the necessity of hierarchical guidance.

During the overall design phase of solar-powered unmanned aerial vehicles (UAVs), a large amount of high-fidelity (HF) propeller aerodynamic performance data is required to enhance design performance, but the acquisition cost is prohibitively expensive. To improve model accuracy and reduce modeling costs, this paper constructs a multi-fidelity aerodynamic data fusion model by associating data with different fidelity. This model utilizes a low-fidelity computational method to quickly determine the design space. The constrained Latin hypercube sampling based on the successive local enumeration (SLE-CLHS) method and the expected improvement (EI) criterion were adopted to achieve the efficient initialization and fastest convergence of the Co-Kriging surrogate model within the design space. This modeling framework was applied to acquire the aerodynamic performance of high-altitude propellers, and the model was evaluated using various performance indicators. The results demonstrate that the proposed model has excellent predictive performance. Specifically, when the surrogate model was constructed using 350 high-fidelity samples, there were improvements of 13.727%, 12.241%, and 5.484% for thrust, torque, and efficiency compared with the surrogate model constructed from low-fidelity samples.

Multi-UAV cooperative encirclement tasks have attracted considerable attention in areas such as military defense and target interception. Fixed-wing UAVs face substantial challenges due to intrinsic dynamic limits, including their minimum velocity and turning radius, particularly when engaging evasive target and navigating in obstacle environments. This paper presents a hybrid deep reinforcement learning approach, in which a cooperative task environment is developed for fixed-wing UAVs that jointly integrates encirclement and obstacle avoidance. A composite MADDPG framework enhanced with curriculum learning is designed, employing progressive task staging and reward optimization to accelerate convergence and improve policy stability. Simulation results demonstrate that the proposed method achieves single-step encirclement success rates exceeding 80% in complex environments, while maintaining 10-step success rates around 70%, thereby strengthening both encirclement capability and obstacle avoidance safety in fixed-wing UAV swarm. This study provides new insights into the intelligent cooperative control of fixed-wing UAVs in high-risk missions.

According to the static aeroelastic characteristics of Solar-Powered UAV (SP-UAV) with large aspect ratio, this paper develops a nolinear aeroelastic analysis method which takes into account both the calculation accuracy and efficiency. Firstly, we establish the finite element model of the geometric nonlinear structure, which is verified by the static test of the whole UAV to reduce the calculation error caused by the simplified method of structure model. Then, the aerodynamic model of SP-UAV is established by using Curved Vortex Lattice (CVL), and the structure-aerodynamic coupling interpolation is realized by the Thin Plate Spline-interpolation (TPS) method, so as to complete the full static aeroelastic analysis. The analysis indicate that the calculated results of the longitudinal trim are well match the first flight condition. In particular, considering the elevator efficiency loss occurred in longitudinal trim analysis of the UAV, we design an optimization scheme to improve the efficiency of the elevator by about 15%. The simulation results show that the loss of elevator efficiency of flexible solar UAV with high aspect ratio is non-negligible, with the maximum value higher than 40% under high dynamic pressure. In actual flight, the speed of UAV must be reduced as much as possible to minimize the loss of rudder efficiency, while the elastic control method should be considered in flight control.