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.

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 5 mm 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, 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.