Di Liu

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).

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.