In this paper, a novel model predictive control (MPC) method based on the population normal probability division genetic algorithm and ant colony optimization (GA-ACO) method is proposed to optimally solve the problem of standard MPC with constraints that generally cannot yield global optimal solutions when using quadratic programming (QP). Combined with dynamic sliding mode control (SMC), this model is applied to the dynamic trajectory tracking control of autonomous underwater vehicles (AUVs). First, the computational fluid dynamics (CFD) simulation platform ANSYS Fluent is used to solve for the main hydrodynamic coefficients required to establish the AUV dynamic model. Then, the novel model predictive controller is used to obtain the desired velocity command of the AUV. To reduce the influence of external interference and realize accurate velocity tracking, dynamic SMC is used to obtain the control input command. In addition, stability analysis based on the Lyapunov method proves the asymptotic stability of the controller. Finally, the trajectory tracking performance of the AUV in an underwater, three-dimensional environment is verified by using the MATLAB/Simulink simulation platform. The results verify the effectiveness and robustness of the proposed control method.

Autonomous underwater vehicles (AUVs) can replace human beings in seabed organisms fishing in complex and dangerous ocean environments, with high fishing efficiency and economic benefits. Therefore, the design of a reasonable area complete coverage path planning (CCPP) algorithm is the key technology for realization of autonomous fishing. To address this problem, this paper proposes a new generalized complete coverage path planner. First, this planner discretizes the seabed topography of the fishing area and establishes a two-dimensional (2-D) grid map model containing height information. Second, a global path planning algorithm based on the Improved Whale Optimization Algorithm (IWOA) is introduced, and a local path optimization is performed for the turning portion of the planned path based on the dynamics and fishing characteristics of offshore fishing AUV. Then, for the engineering practical problem that offshore fishing AUVs have limited self-contained energy and difficulty in energy replenishment, an adaptation fitness function with multiple constraints is established as the evaluation model of the optimal path by considering the length of the navigation path, turning angle, altitude change, as well as coverage and repetition rate. Finally, the performance of the proposed CCPP algorithm is simulated and verified.

The frequency characteristics of cavitation fluctuations in high-speed cavitating jets are intricate due to the coupling mechanisms of unsteady behaviors. This study employs high-speed photography to experimentally investigate the relationship between frequency characteristics and the unsteady behavior of cavitating jets with various cavitation numbers. Temporal evolution patterns of the cavitating jets are analyzed through spatiotemporal (s-t) diagrams. The spatial distribution and temporal evolution of cavitation fluctuation frequencies are examined using fast Fourier transform (FFT) and continuous wavelet transform (CWT), respectively. Proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) are employed to identify coherent structures and their corresponding frequencies. In results, the s-t diagrams reveal the distinct regions influenced by cavitation shedding and collapse. FFT results indicate that upstream of the jet trajectory, spectral energy is concentrated in the shedding band, shifting toward lower frequencies with increasing axial distance. The CWT spectrum exhibits a single peak in the upstream, identifying it as the shedding frequency. POD modes associated with shedding dominate the energy contribution at higher cavitation numbers, while they become less prominent at lower cavitation numbers. DMD extracts and identifies coherent structures associated with shedding through frequency-specific decomposition.

In this paper, a model predictive control (MPC) method optimized by an adaptive particle swarm optimization (APSO) algorithm is proposed. Combined with non-singular terminal sliding mode control (NTSMC), the inner and outer double-closed-loop control system is constructed to solve the fully actuated autonomous underwater vehicle (AUV) dynamic trajectory tracking control problem. First, the outer loop controller generates the expected optimal velocity commands and passes them to the inner loop velocity controller, which generates the available control inputs to ensure the entire closed-loop trajectory tracking. In the controller design stage, system input and state constraints are effectively considered. After that, a compensator based on an adaptive radial basis function (RBF) neural network (NN) is designed to compensate for the model error and external sea state disturbances and to improve the control accuracy of the system. Then, the stability of the proposed controller is proved based on Lyapunov analysis. Finally, the dynamic trajectory tracking performance of an AUV with different sea state disturbances is verified by simulation, and the simulation results are compared with double-closed-loop PD control and cascade control of standard MPC based on PSO and SMC. The results show that the designed controller is effective and robust.