To address the swing suppression control problem for wing rotation process of UAV towed aerial recovery assembly, an anti-swing control method based on appointed-time preset performance control is proposed. Firstly, the characteristics of the aerial recovery assembly are analyzed. A six degree of freedom nonlinear model of the aerial recovery assembly is established using the Newton-Euler method, the additional forces and moments caused by the wing rotation are analyzed, and additional force and additional moment models containing the wing rotation angle are established. Secondly, in response to the strong nonlinear characteristics of aerodynamic force and aerodynamic torque changes during wing rotation, this paper adopts unsteady computational fluid dynamics methods to calculate the aerodynamic force and moments during this process, and establishes aerodynamic force and aerodynamic moments models containing the wing rotation angle. On this basis, the six degrees of freedom nonlinear models are transformed to affine nonlinear form, and a composite anti-swing control method integrating a non-constraining force vector sum direction feedforward compensation controller with an appointed-time prescribed performance control feedback controller is proposed. A simulation is carried out and the results show that, the maximum longitudinal displacement of the proposed method is 0.16 m, and the maximum lateral displacement is 0.18 m, demonstrating significant advantages of the proposed method.

To enhance fault-tolerance capability and mission completion ability of hypersonic vehicles under various severity levels of actuator faults, this paper proposes a learning-based hierarchical coordination fault-tolerant method for hypersonic vehicles. Firstly, to achieve online quantitative analysis of the severity of actuator faults, deep neural network-based approaches for predicting vehicle trim capability and reachable region boundaries are proposed. Subsequently, using the above prediction methods as “bridges”, a hierarchical coordinated fault-tolerance framework is constructed. The method assesses the severity of current actuator faults in real-time based on prediction results, then selectively coordinates fault-tolerant mechanisms at the control, guidance, and planning layers to mitigate the impact of actuator faults on flight performance and mission completion capability as much as possible. Finally, simulations under three different severity levels of actuator faults are conducted to validate the effectiveness of the proposed fault-tolerant method.

This article investigates the approaching control for fixed-wing Unmanned Aerial Vehicle (UAV) aerial recovery in the presence of pre-specified performance requirements, complex airflows, maneuvering flight of transport aircraft, and different initial deviations. First, a novel control-oriented Six-Degree-Of-Freedom (6-DOF) UAV model considering airflow disturbances is established for better consistency with the actual UAV system. Then, to achieve satisfactory performance in the approaching process, a Flexible Appointed-time Prescribed Performance Control (FAPPC) algorithm, with the features of user-specified time convergence, no overshoot, independence from the initial value, and singularity-free, is proposed. Specifically, to solve the singularity issue encountered by the existing PPC methods in dealing with sudden disturbances, an adaptive adjustment signal is introduced in FAPPC to perceive the threat of increasing error and relax the preset boundaries appropriately. Moreover, minimum learning parameter-based neural network estimators are developed to approximate unknown lumped disturbances at a low computational cost. Finally, the stability of the closed system is analyzed via Lyapunov synthesis, and the effectiveness and advantages of the proposed control scheme are demonstrated via simulation and Hardware-In-the-Loop (HIL) experimental validation.

Aiming to address the Unmanned Aerial Vehicle (UAV) formation collision avoidance problem in Three-Dimensional (3-D) low-altitude environments where dense various obstacles exist, a fluid-based path planning framework named the Formation Interfered Fluid Dynamical System (FIFDS) with Moderate Evasive Maneuver Strategy (MEMS) is proposed in this study. First, the UAV formation collision avoidance problem including quantifiable performance indexes is formulated. Second, inspired by the phenomenon of fluids continuously flowing while bypassing objects, the FIFDS for multiple UAVs is presented, which contains a Parallel Streamline Tracking (PST) method for formation keeping and the traditional IFDS for collision avoidance. Third, to rationally balance flight safety and collision avoidance cost, MEMS is proposed to generate moderate evasive maneuvers that match up with collision risks. Comprehensively containing the time and distance safety information, the 3-D dynamic collision regions are modeled for collision prediction. Then, the moderate evasive maneuver principle is refined, which provides criterions of the maneuver amplitude and direction. On this basis, an analytical parameter mapping mechanism is designed to online optimize IFDS parameters. Finally, the performance of the proposed method is validated by comparative simulation results and real flight experiments using fixed-wing UAVs.

To address the problem of no-fly zone and interceptor avoidance during the reentry phase, a learning-based integrated fault-tolerant guidance and control method for hypersonic vehicles considering avoidance and penetration is proposed. Firstly, to improve the guidance performance of the vehicle in longitudinal guidance, the six degree of freedom model of the vehicle, which includes the attitude fault-tolerant control system, is adopted based on the integrated design concept of guidance and control to predict the range-to-go. In view of the resulting problem of time-consuming in calculation, the deep network is further used to fit the predicting process, so as to improve the real-timeliness of the algorithm. Then, to solve the problem of no-fly zone and interceptor avoidance in lateral guidance, the Interfered Fluid Dynamical System (IFDS) algorithm is introduced to calculate the expected heading angle of the vehicle considering threat factors, and the heading angle error corridor and the flip logic of the bank angle are used to obtain the command, so that the vehicle has the ability of avoidance and penetration. Next, considering the complexity of flight environment, the difference of maneuvering ability of vehicle during the reentry phase, and the influence of dynamic characteristics of attitude control system on guidance performance, the deep reinforcement learning algorithm is employed to train the agent, so that the agent can make online decisions on key parameters of IFDS algorithm according to the real-time state of vehicle, and improve the effectiveness and adaptability of the proposed guidance method. Finally, the results of semi-physical comparison simulation and Monte Carlo simulation show that the proposed method can achieve high performance in avoidance, penetration and guidance.