Autonomous underwater vehicle (AUV) is one of the most important exploration tools in the ocean underwater environment, whose movement is realized by the underwater thrusters, however, the thruster fault happens frequently in engineering practice. Ocean currents perturbations could produce noise for thruster fault diagnosis, in order to solve the thruster fault diagnostics, a possibilistic fuzzy C-means (PFCM) algorithm is proposed to realize the fault classification in this paper. On the basis of the results of fault diagnostics, a fuzzy control strategy is proposed to solve the fault tolerant control for AUV. Considering the uncertainty of ocean currents, it proposes a min-max robust optimization problem to optimize the fuzzy controller, which is solved by a cooperative particle swarm optimization (CPSO) algorithm. Simulation and underwater experiments are used to verify the accuracy and feasibility of the proposed method of thruster fault diagnostics and fault tolerant control.

The bionic robotic fish is one of the special autonomous underwater vehicles (AUV), whose path planning is crucial for many applications including underwater environment detection, archaeology, pipeline leak detection, and so on. However, the uncertain ocean currents increase the difficulty of path planning for bionic robotic fish in practice. In this paper, the path energy consumption is selected as the objective function for path planning, path safety factor, and smoothness are considered as the constraint conditions. The kinematic model is established for bionic robotic fish and, considering the uncertainty of ocean currents, a “min-max” robust optimization problem is proposed in the light of the normal optimization model of path planning for bionic robotic fish. The co-evolutionary genetic algorithm is presented to solve the robust optimization problem with two populations; one population represents the solutions and the other represents the uncertain ocean currents. The objective of the proposed algorithm is to find a robust solution that has the best worst-case performance over a set of possible ocean currents. Multiple experiments indicate that the proposed algorithm is very effective for path planning for bionic robotic fish with ocean currents.

Autonomous underwater vehicles (AUVs) are increasingly being used in missions involving submarine cable detection, underwater archaeology, pipeline inspection, military reconnaissance, and so on. It is very important to realize AUV path tracking to accomplish these missions. In this paper, a fuzzy controller based on the established kinematic and dynamic models of AUV systems is presented to solve the AUV path-tracking problem. In order to design the fuzzy controller to exhibit good performance, we select the path length, smoothness, and cross-track position error as the multiple optimization performance indexes for the fuzzy controller. We propose the particle swarm optimization (PSO) algorithm to determine the parameters of the membership functions. Different scenarios are presented to test the performance of the proposed algorithm, including the straight line, sine curve, half-moon shape, Archimedean spiral, and practical paths. The results are given to illustrate the effectiveness and feasibility of the fuzzy controller with the optimization of multiple performance indexes.

This study investigates the WAM-V (Wave Adaptive Modular Vessel) catamaran configuration, focusing on the hydrodynamic interaction between its articulated hulls. The unique hinged connection mechanism induces a relative angular displacement between the demihulls during operation, significantly modifying the calm water resistance characteristics. Such resistance variations critically influence both vessel maneuverability and the operational effectiveness of onboard acoustic detection systems. This study using computational fluid dynamics (CFD) technology, the effects of varying demihull spacing and the angles of the demihulls on resistance were calculated. Numerical simulations were performed using STAR-CCM+, employing the Reynolds-averaged Navier–Stokes equations (RANS) method combined with the k-epsilon turbulence model. The study investigates the free surface and double body viscous flow at different Froude numbers in the range of 0.3 to 0.75. The analysis focuses on the effects of the demihull spacing ratio (B_S/L_PP, Demihull spacing/Length between perpendiculars) on calm water resistance. Specifically, the resistance coefficient at B_S/L_PP = 0.2 is on average 14% higher than that at B_S/L_PP = 0.5. Additionally, the influence of demihull angles on resistance was simulated at B_S/L_PP = 0.42. The results indicate that inner demihull angles result in higher resistance compared to outer angles, with the maximum increase in resistance being approximately 9%, with specific outer angles effectively reducing resistance. This study provides a scientific basis for optimizing catamaran design and offers valuable insights for enhancing sailing performance.