In recent years, a rising number of incidents between Unmanned Aerial Vehicles (UAVs) and planes have been reported at airports and airfields. A design scheme for an airport obstacle-free zone monitoring UAV system based on computer vision is proposed. The system integrates the functions of identification, tracking, and expelling and is mainly used for low-cost control of balloon airborne objects and small aircrafts. First, a quadcopter dynamic model and 2-Degrees of Freedom (2-DOF) Pan/Tilt/Zoom (PTZ) model are analyzed, and an attitude back-stepping controller based on disturbance compensation is designed. Second, a low and slow small-target self-identification and tracking technology is constructed against a complex environment. Based on the You Only Look Once (YOLO) and Kernel Correlation Filter (KCF) algorithms, an autonomous target recognition and high-speed tracking plan with great robustness and high reliability is designed. Third, a PTZ controller and automatic aiming strategy based on Anti-Windup Proportional Integral Derivative (PID) algorithm is designed, and a simplified, automatic-aiming expelling device, the environmentally friendly gel ball blaster, which features high speed and high accuracy, is built. The feasibility and stability of the project can be verified through prototype experiments.

Carrier-based aircraft landing involves complex system engineering characterised by strong nonlinearity, significant coupling and susceptibility to environmental disturbances, and autonomous landing of carrier-based aircraft under fault states is even more challenging and riskier. To address the control-system problems of loss of efficiency and performance due to actuator faults and performance degradation due to various unknown disturbances, presented here is fault-tolerant control for carrier-based aircraft based on adaptive fuzzy sliding-mode fault-tolerant control (AFSMFTC). First, three models are built (the carrier-based aircraft fault model, the carrier air wake model and the deck motion model), and the control framework of the autonomous landing control system is introduced. Next, a longitudinal and lateral flight channel controller comprising an adaptive fuzzy network, adaptive laws and a sliding-mode controller is designed using the AFSMFTC method. The adaptive fuzzy network implements fuzzy approximation for the sliding-mode switching terms to further offset errors induced by unknown disturbances, the adaptive laws compensate for actuator faults, and the sliding-mode controller ensures tracking of the overall flight path. Furthermore, the stability of the fault-tolerant method is demonstrated using the Lyapunov function. Finally, simulation and comparative experiments show that the proposed fault-tolerant method has outstanding control performance and strong fault-tolerant capability, thereby providing an effective and feasible solution for designing an autonomous landing system for carrier-based aircraft under fault states.

This study proposes an aerial recovery technology that can complete aerial recovery for target unmanned aerial vehicles (UAVs) by a platform UAV to overcome the limitation of ground recovery for multi‐rotor UAVs. To ensure the safety of automatic aerial recovery, more requirements are made for the stability of multi‐rotor UAVs. This study proposes a back‐stepping trajectory tracking controller for feedforward compensation of uncertain disturbances. The results of the simulation show that the controller can effectively prevent external disturbance and meet requirements for the desired trajectory tracking. Furthermore, based on a you only look once (YOLO) network, this study designs a real‐time image recognition and positioning system, which can achieve real‐time positioning for platform UAV by the target UAV with an image‐processing rate of 8 frames per second (FPS). With the main objective of safety, this study designs an automatic recovery guiding system for target UAVs using a fuzzy controller. The target UAVs can automatically realize a soft landing on the platform UAV effect of the guiding system. Finally, this study conducts dynamic recovery tests in‐flight and verifies the feasibility of automatic aerial recovery technology for multi‐rotor UAVs.

Carrier-based aircraft (CBA) landing involves complex system engineering characterized by strong non-linearity, significant coupling and susceptibility to environmental disturbances. To address uncertainties in parameters, carrier air-wake disturbances and other challenges inherent to CBA landing, this paper presents a longitudinal automatic landing system based on adaptive fuzzy sliding-mode control. This system was developed to improve control accuracy and stability during the critical landing phase. Furthermore, this paper analyzes components of carrier air-wake and motion conditions for ideal landing points on the carrier deck, and designs a sliding-mode surface with the integral term. An adaptive fuzzy sliding-mode controller based on equivalent and switching controls is constructed, which exhibits stability under the Lyapunov stability condition. Moreover, a Monte Carlo simulation method is employed to verify the simulation of the automatic landing control system. Owing to its impressive dynamic performance and robustness, the proposed control method can track expected values with high accuracy in a complex environment, thereby satisfying the CBA landing requirements.