To enable a carrier-based unmanned aerial vehicle (UAV) to track the desired glide trajectory and safely land on the deck with the presence of system faults, this paper proposes a neural network-based adaptive sliding mode fault-tolerant control (NASFTC) method. Firstly, the dynamic model of the carrier -based UAV, the actuator fault model, the additional unknown fault model, and the control framework of the automatic carrier landing system (ACLS) were developed. Subsequently, controllers for both longitudinal and lateral channels were designed by using the NASFTC method. The controller consists of three parts: the adaptive laws for compensating the actuator faults, the RBF neural network for compensating the additional unknown faults, and the sliding mode method for ensuring overall trajectory tracking. Then, the Lyapunov function theorem was applied to carry out the stability analysis. Finally, comparative simulations under three different scenarios were conducted. The comparative results show the effectiveness of the proposed NASFTC method, which has fault-tolerant ability and can successfully control the aircraft to execute carrier landing task regardless of the actuator partial loss fault and the additional unknown fault.

As an amphibious system, aerial-terrestrial vehicles have demonstrated remarkable environmental adaptability. In order to expand their synergistic advantages, in this letter, an Aerial-Terrestrial vehicle consisting of a pair of unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) is proposed, which is capable of performing UAV-UGV autonomous docking and detaching. Under the docking modality, the proposed vehicle can perform amphibious locomotion to adapt to rugged terrains. Under the detaching mode, the distributed UAV and UGV subsystems gain enhanced accessibility to the confined space and can share both air and land information, which is promising to accomplish collaborative missions. In order to achieve the docking and detaching of the two subsystems, a screw-like binding mechanism is proposed. To implement the binding mechanism, it is separated into two pieces, i.e., ‘screw' and ‘nut,’ which are duel-functioned as the landing gear of the UAV or the UGV roof, respectively. To improve the success rate of docking, a mathematical model of the binding mechanism is established to calculate the ideal curve of the guiding surface, which provides important cues for the mechanism fabrication. With such a binding mechanism, UAV or UGV can execute docking or detachment via screwing-like self-spinning motion. The spinning torque of the UAV and UGV are calibrated by torque measurement tests. As a result, each independent vehicle can generate sufficient power to lock or detach via its original actuators, thus omitting the extra actuators. The full stage control, including air-ground docking-detaching and amphibious locomotion, are validated experimentally.