Underwater vehicles (UVs) are subjected to various environmental disturbances due to ocean currents, propulsion systems, and un-modeled disturbances. In practice, it is very challenging to design a control system to maintain UVs stayed at the desired static position permanently under these conditions. Therefore, in this study, a nonlinear dynamics and robust positioning control of the over-actuated autonomous underwater vehicle (AUV) under the effects of ocean current and model uncertainties are presented. First, a motion equation of the over-actuated AUV under the effects of ocean current disturbances is established, and a trajectory generation of the over-actuated AUV heading angle is constructed based on the line of sight (LOS) algorithm. Second, a dynamic positioning (DP) control system based on motion control and an allocation control is proposed. For this, motion control of the over-actuated AUV based on the dynamic sliding mode control (DSMC) theory is adopted to improve the system robustness under the effects of the ocean current and model uncertainties. In addition, the stability of the system is proved based on Lyapunov criteria. Then, using the generalized forces generated from the motion control module, two different methods for optimal allocation control module: the least square (LS) method and quadratic programming (QP) method are developed to distribute a proper thrust to each thruster of the over-actuated AUV. Simulation studies are conducted to examine the effectiveness and robustness of the proposed DP controller. The results show that the proposed DP controller using the QP algorithm provides higher stability with smaller steady-state error and stronger robustness.

Nowadays, underwater vehicles (UVs) are applied to various tasks such as carrying objects or maintenance of underwater structures. To carry out well these tasks, UVs should keep the position and orientation in the water to perform the specified tasks. However, the systems used in underwater operations are always under the influence of disturbance such as ocean currents and model uncertainties. In this paper, the robust station-keeping (SK) control algorithm based on a sliding mode control (SMC) theory is designed to guarantee stability and better performance of a hovering over-actuated autonomous underwater vehicle (HAUV) despite the existence of model uncertainties and ocean current disturbance in the horizontal plane (HP). Using the Lyapunov theorem, the stability of the proposed controller is demonstrated. Besides, an optimal allocation control (AC) algorithm is also designed to keep the linear position and Euler angles of the HAUV in the presence of model uncertainties as well as ocean currents and to minimize the energy consumption of the system. Finally, a series of simulations and experiments for the HAUV system is conducted to demonstrate the superior performance of the proposed method.

Dimensions, volume, and mass of agricultural goods are essential physical features to build sizing, grading, and packaging systems. Manual property measurements are time-consuming, costly, and labor-intensive due to traditional technologies while various devices need to be installed to adapt process requirements. Furthermore, it is very challenging to accurately measure product with irregular or specific shapes, such as starfruit (Averrhoa carambola). Recently, there have been several research results on estimating features of the irregularly shaped object, they are either get inaccurate results or need repeated captures, computational resources, and time to rebuild the three-dimensional representation of the goods. The starfruit has not been studied completely in this size and weight measurements. This paper focuses on new techniques which exhibit simple installation to generate multiple functions for estimating the dimensions, volume, and mass with high accuracy. In this proposed method, we separated the process into two main phases. In the first phase, a camera is used to capture a top-view image of a starfruit, then image processing and machine vision techniques are applied to process the acquisition image before the process slices numerically the starfruit into several pieces along the longitudinal axis and estimates the physical attributes of each pieces using disc method and conical frustum method. Its volume is the summation of the volume of each partial slice. In the next phase, the density is used to estimate the mass of starfruit since the correlation coefficient (R-squared) between the volume and mass of starfruit is nearly linear with 0.9205. The validated results are highly competitive with accuracy of about 99% for the volume and mass in 300 testing samples.

On inland waterways the ship resistance and propulsive characteristics are strictly related to the depth of the waterway, thus it is important to have an understanding of the influence of water depth on ship hydrodynamic characteristics. Therefore, accurate predictions of hydrodynamic forces in restricted waterways are required and important. The aim of this paper is investigating the capability of the commercial unsteady Reynolds–Averaged Navier–Stokes (RANS) solver to predict the influence of water depth on ship resistance. The volume of fluid method (VOF) is applied to simulate the free surface flow around the ship. The hull resistance in shallow and deep water is compared. The obtained numerical results are validated against related experimental studies available in the literature.