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Encircling and capturing foreign unmanned underwater vehicles (UUVs) by multiple defensive UUVs has recently attracted increasing attention. Previous studies using a self-organized map neural network for target allocation suffer from team imbalance and heavy communication burden problems. In addition, the cooperation of defensive UUVs in a team is insufficient. Aiming at these problems, this article forms balanced encircling teams with the estimated travel time acquired in local communication through distributed auction. The quantum particle swarm optimization algorithm is used to optimize the initial encircling circle for fast encirclement, and then dynamically shrink the encircling circle to improve cooperation. Finally, the encirclement control of the defensive UUV is achieved by a fast marching model predictive velocity planner and a proportional–integral controller. In real multi-island waters, this article adopts the proposed algorithm to simulate the encircling of foreign UUVs, which have the same speed as that of defensive UUVs. The simulation results show that the algorithm has advantages in shortening the chasing distance and improving the encircling efficiency. A pool validation experiment is also conducted to verify the effectiveness of the algorithm.

Underwater vehicles are effective and irreplaceable vehicles for exploring and developing the underwater world, and their level of development has become an important symbol for evaluating a country’s maritime strength and comprehensive national power. Underwater thrusters are the main power components of underwater vehicles. The performance of fault diagnosis for underwater thrusters directly affects the safety and reliability of underwater vehicles. In this article, a fault diagnostic approach for thrusters combining data augmentation by generative adversarial network (GAN) and fault identification by improved temporal convolutional network (ITCN) is proposed. In order to solve the problem that real samples of propeller entanglement and transmission anomaly faults are difficult to be collected completely, a data augmentation theory relying on controllable generation of thruster signals is proposed according to the prior knowledge of these two fault modes. And a GAN for underwater thruster signals (GAN-UTS) is designed as an implementation of the proposed data augmentation theory. Considering the slightly insufficient accuracy of the original TCN in identifying some fault modes, a fault classifier based on ITCN is proposed to further improve the overall accuracy of fault identification. The experiments show that the diagnostic performance is significantly improved by jointly using the GAN-UTS-based data augmentation and ITCN-based fault identification. Compared with the original TCN-based fault classifier without data augmentation, the average F1 score of the proposed approach is raised by about 8.590%, fully proving the effectiveness of the approach.

In this article, aiming at the characteristics of model uncertainty and low-speed of human-occupied vehicle (HOV) system, a cascaded controller in ocean current environment is proposed. First, the kinematics controller is designed by improved chaotic firefly algorithm (ICFA)-model predictive control (MPC) to get the speed control signal. The multivariable constraint capability of ICFA-MPC can solve the problem of speed jump caused by thruster fault and further solve the problem of driving saturation. Second, the kinematic controller is combined with the dynamics controller which is according to the adaptive algorithm. The adjustable control parameters capability of the adaptive control can further obtain the dynamic control law under the influence of ocean current. Finally, the hybrid allocation strategy based on ICFA is used to redistribute the thruster force/moments. Simulation results displayed the dynamic trajectory tracking fault-tolerant controller can steadily track the reference trajectory under thruster fault without tracking disturbance, speed jump, and driving saturation problem.