In contrast with most existing results concerning unmanned aerial vehicles (UAVs) wherein material points or only attitude/longitudinal dynamics are considered, this article proposes a distributed fixed-time fault-tolerant control methodology for networked fixed-wing UAVs whose dynamics are six-degree-of-freedom with twelf-state-variables subject to actuator faults and full-state constraints. More precisely, state transformations with the scaling function are devised to keep the involved velocity and attitude within their corresponding constraints. The fixed-time property is obtained in the sense of guaranteeing that the settling time is lower bounded by a positive constant, which is independent of initial states. The actuator faults as well as the network induced errors are handled via the bound estimation approach and well-defined smooth functions. By strict Lyapunov arguments, all closed-loop signals are proved to be semiglobally uniformly ultimately bounded, and the tracking errors of velocity and attitude converge to the residual sets around origin within a fixed time.

AUV docking requires the platform to have both wide range cruising and accurate operating abilities, to against challenges of ocean currents, obstacles and constraints. This paper proposed an evolutionary-based method, for the purpose of docking path optimization. First, the ocean environment and constraints are analysed and modelled. Next, the control points are designed to satisfy the model constraints. Then, the adaptive law and quantum behaviour are introduced in Particle Swarm Optimization (PSO), to achieve the global time-optimization. Finally, the proposed approach is evaluated via Monte-Carlo trials, which demonstrates a significant improvement with respect to the state-of-the-art approaches.

This paper proposes a neural network-based nonsingular terminal sliding mode controller with prescribed performances for the target tracking problem of underactuated underwater robots. Firstly, the mathematical formulation of the target tracking problem is presented with an underactuated underwater robot model and the corresponding control objectives. Then, the target tracking errors from the line-of-sight guidance law are transformed using the prescribed performance technique to achieve good dynamic performance and steady-state performance that meet the pre-set conditions. Meanwhile, considering the model’s uncertainties and the external disturbances to the underwater robots, a target tracking controller is proposed based on the radial basis function (RBF) neural network and the non-singular terminal sliding mode control. Lyapunov stability analysis and homogeneity theory prove the tracking errors can converge on a small region that contains the origin with prescribed performance in finite time. In the simulation comparison, the controller proposed in this paper had better dynamic performance, steady-state performance and chattering supression. In particular, the steady-state error of the tracking error was lower, and the convergence time of the tracking error in the vertical distance was reduced by 19.1%.

The accurate target detection under different environmental conditions and the real-time target positioning are vital for the successful accomplishment of underwater missions of Remotely operated vehicles (ROVs). In this paper, we propose a vision-based underwater target detection and positioning approach to detect and estimate the position and attitude of artificial underwater targets. The proposed approach is composed of an underwater target detection algorithm YOLO-T and a target positioning algorithm. Firstly, we modify the structure of YOLOv5 algorithm using Ghost module and SE attention module to improve the calculation time of target detection. Secondly, a series of image processing operations are performed on the improved YOLOv5 detection results to increase the detection accuracy. Thirdly, a cooperative marker is designed as the artificial underwater target, and the corresponding positioning algorithm is presented to calculated the position and attitude of the target according to the geometric information of the designed marker. We test our approach through experimental tests a water tank in the laboratory, an anechoic tank and the sea trial in the Huanghai sea in China. The results demonstrate the accurate performance of the proposed detection and positioning method.

This paper proposes a trajectory tracking control scheme consisting of a fast finite-time super-twisting sliding mode control (FSTSMC) approach and an extended state higher-order sliding mode observer (ESHSMO) for unmanned underwater vehicles (UUVs) with external disturbances and model uncertainties. Firstly, an extended state higher-order sliding mode observer with the finite-time convergence is designed based on the higher-order sliding mode technique and the extended state observer technique. Next, on the basis of disturbances and model uncertainties observation, a fast finite-time super-twisting sliding mode control approach is proposed, and the finite time stabilization property of the tracking errors is proved by Lyapunov theory. Finally, through numerical simulation and experiment in a water pool, it has been verified that the proposed control scheme has achieved the high control precision, the smaller chattering, the disturbance compensation and the fast finite-time convergence in UUV trajectory tracking.