High-aspect-ratio aircraft are widely used in military and civilian fields, such as reconnaissance, surveillance, and attacks, due to their high lift-to-drag ratio, strong payload capability, significant endurance effect, and good stealth performance. However, compared to conventional aircraft, high-aspect-ratio aircraft are more susceptible to gust disturbances during flight. In response to this phenomenon, a full-scale dynamic model of a high-aspect-ratio unmanned aerial vehicle was developed. Considering the coupling among control surfaces, structural forces, and aerodynamic forces, along with sensor, actuator, and delay effects, an H_∞ control law was designed using the principle of singular value energy flow reduction and weighted function, with a PID(Pro portional-Integral-Derivative) control law for comparison. The two controllers were then subjected to pulse-response and jury stability tests. Finally, wind tunnel tests were conducted to investigate the gust alleviation principle, in which gust disturbances were generated using gust generators and control surface self-excitation. The results present that the average wing root bending moment and wing tip overload under the PID control law decrease by approximately 30%, while under the H_∞ control law, both the average wing root bending moment and wing tip overload reduction rate exceed 50%, with peaks reaching 60%. This validates the feasibility and efficiency of the designed H_∞ controller.

This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.

In this study, the effects of solar wind on an electric sail (E-sail) are modeled and analyzed using an absolute nodal coordinate formulation (ANCF). First, the thrust of the charged metal tether that makes up the E-sail was analyzed and a model was established. Numerical simulations of a single metal tether were performed. Then, an overall E-sail model was established using the connection matrix, and E-sails subjected to different angular velocities were compared. Simulation results of the ANCF model and a dumbbell model were compared at different angular velocities. The results confirm that with a relatively high angular velocity, the flexible metal chain can be approximately regarded as a rigid body. However, with a small angular velocity, the flexibility of the metal chain cannot be ignored.