Han Bao

Tethered unmanned surface vehicle (USV) and remotely operated vehicle (ROV) systems are widely used in deep-sea inspection, observation, and intervention tasks. During cooperative operations, the USV must follow the mission trajectory of the ROV while avoiding surface obstacles and maintaining a prescribed tether-related safety envelope. This study proposes a multi-objective nonlinear model predictive control (NMPC) framework for USV-side cooperative tracking and dynamic obstacle avoidance in tethered USV-ROV operations. The framework integrates the predicted three-dimensional ROV trajectory, USV nonholonomic motion, surface-obstacle avoidance, straight-line tether-length-related geometric constraints, and control-smoothness regulation into a unified receding-horizon optimization problem. Sequential Least Squares Programming is used to compute the online control sequence. Numerical simulations include obstacle-free tracking under bounded ocean-current disturbances and heterogeneous surface–underwater obstacle scenarios. The results show that the proposed controller provides improved tracking performance and maintains the straight-line tether-length proxy below the prescribed limit in the tested simulations. The current-disturbance results further indicate preliminary disturbance-rejection capability under bounded time-varying ocean currents. The study provides a controller-level numerical framework for cooperative tracking and surface obstacle avoidance in tethered USV-ROV operations.

This study investigates the hydrodynamics of the net system of the bottom-mounted aquaculture farms located in the Bohai Sea, addressing the growing demand for high-quality aquatic products and the limitations of coastal aquaculture. Based on the validation part, the established lumped-mass method integrated with the finite element method ABAQUS/AQUA was employed to evaluate the structural responses of the net system with three arrangement schemes under diverse environmental loads. The hydrodynamic loads on net twines are modeled with Morison formulae. With the motivation of investigating the trade-offs between volume expansions, load distributions, and structural reliabilities, Scheme 1 refers to the baseline design enclosing the basic aquaculture volume, while Scheme 2 targets to increase the aquaculture volume and utilization rate and Scheme 3 seeks to optimize the load distributions instead. The results demonstrate that Scheme 1 provides the optimal balance of structural safety and functional efficiency. Specifically, under survival conditions, Scheme 1 reduces peak bottom tension rope loads by 14% compared to Scheme 2 and limits maximum netting displacement to 4.0 m. It is 21.3% lower than Scheme 3, of which the displacement is 5.08 m. It has been confirmed that Scheme 1 effectively minimizes collision risks, whereas the other schemes exhibit severe collisions. Scheme 1 trades off maximum volume expansion for optimal load management, minimal deformation, and the highest overall structural reliability, making it the recommended design. These findings offer valuable insights for the design and optimization of net systems in offshore aquaculture structures serviced in comparable offshore regions.

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.

This paper studies the three‐dimensional (3‐D) dynamic trajectory tracking control of an autonomous underwater vehicle (AUV). As AUV is a typical nonlinear system, each degree of freedom is strongly coupled, so the traditional control method based on the nominal model of AUV cannot guarantee the accuracy of the control system. To solve this problem, we first propose a prediction model based on a radial basis function neural network (RBF‐NN). The nonlinearity of AUV is learned and modeled offline by RBF‐NN based on previous data. This model can reflect the time sequence state and control variables of AUV. Secondly, to avoid the overfitting problem in network training based on the traditional gradient descent method, a new adaptive chaotic sparrow search algorithm (ACSSA) is proposed to optimize the network parameters, to improve the full approximation ability of RBF‐NN to nonlinear systems. To eliminate the steady‐state error caused by external interference during AUV trajectory tracking, a nonlinear optimizer is designed by updating the deviation of the NN model output layer. In each sampling period, the predictive control law is calculated online according to the deviation between the predicted value and the actual value. In addition, the stability analysis based on the Lyapunov method proves the asymptotic stability of the controller. Finally, the 3‐D dynamic trajectory tracking the performance of AUV under different external disturbances is verified by MATLAB/Simulink, and the results show that the proposed controller is more efficient and robust than the standard model predictive controller (MPC) controller and the standard NN model predictive controller (NNPC).

In this paper, we propose a cubic spline interpolation method to generate a desired curve path and present an integral line of sight (ILOS) method and a control strategy for course tracking control based on nonsingular terminal sliding mode to enable an underwater snake-like robot (USR) to move towards and follow the path generated by the parametric cubic-spline interpolation (PCSI) path-planning method, while considering the disturbances caused by ocean currents. The efficiency of robot locomotion is an important evaluation criterion for robot design. Thus, we introduce a multi-strategy improved sparrow search algorithm (MISSA) to dynamically choose gait parameters that significantly enhance the efficiency of robot movement. We conduct simulations to demonstrate that the proposed controller enables the USR, subjected to ocean currents, to accurately converge towards and follow the target path. Our results also reveal that MISSA effectively enhances the locomotion efficiency of the robot.

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.

This paper focused on the influence of initial imperfection on the critical load of PMMA pressure spherical shell by introducing buckling modes as the initial defect. Four types of pressure spherical shells including complete spherical shell, spherical shell with single penetration, spherical shell with double penetrations, and hyperspherical shell were studied. The distribution of initial imperfections was given through the modal configuration of linear eigenvalue analysis, and then the buckling critical load of the spherical shell was analyzed by using the consistent modal defect method and N-order modal defect method. Results confirmed that: Initial imperfections have a great different influence on pressure spherical shells with varying penetration. It has the greatest influence on the critical load of a spherical shell with double penetration at a 5mm defect. With the increase of defect amplitude, the critical load of the spherical shell decreases. The analysis results also show that the first-order mode is usually not the worst geometric defect configuration of PMMA pressure spherical shell. And the worst mode may appear in higher-order mode due to the influence of penetration, initial defect amplitude, and different thickness-diameter ratio.

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.