In recent years, tail-sitter unmanned aerial vehicles (UAVs), capable of vertical take-off and landing (VTOL) and long-range flight, have garnered extensive attention. However, the challenge of yaw control, particularly for large-scale UAVs, has become a significant obstacle. It is challenging to generate sufficient yaw moments by motor differential thrust without compromising control authority in other channels or increasing mechanical complexity. Therefore, this paper proposes the concept of blown yaw, which utilizes the high-velocity airflow over rudders, induced by the propellers slipstream, to enhance the yaw control torque actively. An over-actuated, hundred-kilogram-class, tail-sitter UAV is designed to validate the effectiveness of the proposed method. To address the control allocation problem introduced by the implementation of blown yaw, an optimization-based control allocation module is developed, capable of precisely mapping the required forces and torques to all actuators. The proposed method, combined with computational fluid dynamics (CFD) simulations, accounts for the propeller model and the significant differences in actuator effectiveness across various flight conditions. Simulation results demonstrate that the proposed blown-yaw method significantly enhances the yaw control performance, achieving an overall energy savings of approximately 8.0% and a 60% reduction in the mean-squared error. Furthermore, the method exhibits robust performance against variations in control parameters and external disturbances.

This study introduces a control framework designed to improve the disturbance rejection capability of unmanned aerial vehicles (UAVs) by integrating learning-augmented disturbance estimation into the conventional composite hierarchical anti-disturbance control (CHADC) architecture. A sensorless freestream velocity estimation scheme is developed to enable real-time freestream velocity estimation without requiring additional hardware. Leveraging this estimation, Gaussian Process Regression (GPR) is employed to accurately model and integrate with model predictive control (MPC) to compensate for wind-induced external forces. Furthermore, an $SO(3)$-based backstepping attitude controller is designed, incorporating a nonlinear disturbance observer (NDO) to actively compensate for external moment disturbances. Rigorous Lyapunov analysis guarantees the bounded convergence of both attitude and estimation errors, ensuring robust performance in the presence of external disturbances. The effectiveness of the proposed method is validated through high-fidelity simulations and real-world flight experiments conducted on a medium-sized tail-sitter UAV. The experimental results demonstrate significant improvements in robustness under complex operating conditions.

As the size of tail-sitter electric vertical take-off and landing (eVTOL) aircraft increases, significant challenges have arisen due to a lower thrust-to-weight ratio, more nonlinear aerodynamics during transition, and slower control response. To support the design and implementation of the flight control system for the large-scale tail-sitter eVTOL Eagle of Taihu 100 (ET100), a one-third-scale, 13 kg subscale technology demonstrator, ET10, was developed. This paper presents the control framework tailored for ET series. In this framework, a refined state-error-based model predictive control (MPC) scheme is proposed. By reformulating the translational dynamics, the coupling between translation and rotation is explicitly captured. Aerodynamic feedforward and efficiency-aware control allocation are integrated to mitigate model uncertainties. A PX4 software-in-the-loop (SITL) platform was constructed by integrating data from computational fluid dynamics (CFD) simulations and experiments to ensure a high-fidelity aerodynamic modeling. Flight simulations were first conducted to evaluate the improved algorithm in terms of tracking performance across various tasks. Next, flight tests of the ET10 were conducted, demonstrating the reliability and robustness of the scheme across the full flight envelope.

In the realm of missile defense systems, the self-sufficient maneuver capacity of missile swarms is pivotal for their survival. Through the analysis of the missile dynamics model, a time-efficient cooperative attack strategy for missile swarm is proposed. Based on the distribution of the attackers and defenders, the collision avoidance against the defenders is considered during the attack process. By analyzing the geometric relationship between the relative velocity vector and relative position vector of the attackers and defenders, the collision avoidance constrains of attacking swarm are redefined. The key point is on adjusting the relative velocity vectors to fall outside the collision cone. This work facilitates high-precision attack toward the target while keeping safe missing distance between other attackers during collision avoidance process. By leveraging an innovative repulsion artificial function, a time-efficient cooperative attack strategy for missile swarm is obtained. Through rigorous simulation, the effectiveness of this cooperative attack strategy is substantiated. Furthermore, by employing Monte Carlo simulation, the success rate of the cooperative attack strategy is assessesed and the optimal configuration for the missile swarm is deduced.