For long-term missions in complex seas, the onboard energy resources of autonomous underwater vehicles (AUVs) are limited. Thus, energy consumption reduction is an important aspect of the study of AUVs. This paper addresses energy consumption reduction using model predictive control (MPC) based on the state space model of AUVs for trajectory tracking control. Unlike the previous approaches, which use a cost function that consists of quadratic deviations of the predicted controlled output from the reference trajectory and quadratic input changes, a term of quadratic energy (i.e., quadratic input) is introduced into the cost function in this paper. Then, the MPC control law with the new cost function is constructed, and an analysis on the effect of the quadratic energy term on the stability is given. Finally, simulation results for depth tracking control are given to demonstrate the feasibility and effectiveness of the improved MPC on energy consumption optimization for AUVs.

A novel underwater long-arm manipulator (ULAM) and its improved small-flow hydraulic driving system (SHDS) are presented in this article for small autonomous underwater vehicle (AUV). In the process of manipulator design, a joint driving device consisting of linear hydraulic cylinder, three-bar linkage, and four-bar linkage mechanism is developed to allow the manipulator to be folded. Since the manipulator is much long, a small joint angle deviation would result in a large end-effector’s position deviation. To solve the problem, a small-flow hydraulic driving device with circulation in closed loop is proposed. And in the long-arm manipulator, the cross-sectional area of manipulator is small. So the diameter of hydraulic pipeline installed inside the manipulator is limited, but the pipeline should also be long enough. In this case, it is common to cause large pressure loss in hydraulic system. And, rather than oil, water is selected as the hydraulic medium due to the low viscosity. In the hydraulic system design, a scheme with circulation in closed loop and improved sealing structure (consisting of O-ring and Glyd-ring) are developed to reduce the internal leakage and maintain a good end-effector’s precision. In addition, the compressibility of water is relatively large, and it would bring bad impact on end-effector’s precision. In this case, the vacuum-treated water is used and it has a good performance in the high-pressure condition. But in the low-pressure condition, the problem is still serious. In order to solve the problem, based on the “vacuum treatment,” a pre-pressurization pressure treatment is added for the vacuum-treated water. All the improved ways are verified by experiment results. Finally, the end-effector’s positioning experiment and trajectory tracking experiment are conducted on manipulator and small AUV, so as to verify the effectiveness and feasibility of the ULAM.

Ocean currents, mechanical collisions and electronic damage can cause faults in an autonomous underwater vehicle (AUV), including sensors and thrusters. For such problems, this paper designs a fault-tolerant controller that is independent of the results of the fault diagnosis. An adaptive reaching law is developed based on sliding mode control to shorten convergence times. For the chattering phenomenon, a weighted hyperbolic tangent function is adopted instead of the traditional sign function in sliding mode control. Simulations are carried out when thruster and sensor fail under the condition of ocean current disturbance, model uncertainty and sensor noise. Comparative simulation results show that the proposed method can accelerate the convergence speed of the state point and improve the trajectory tracking effect of the AUV. Consequently, the effectiveness of the proposed method is confirmed.

To enhance the crypticity and operational efficiency of unmanned underwater vehicle (UUV) charging, we propose an automatic alignment method for an underwater charging platform based on monocular vision recognition. This method accurately identifies the UUV number and guides the charging stake to smoothly insert into the charging port of the UUV through target recognition. To decode the UUV’s identity information, even in challenging imaging conditions, an encryption encoding method containing redundant information and an ArUco code reconstruction method are proposed. To address the challenge of underwater target location determination, a target location determination method was proposed based on deep learning and the law of refraction. The method can determine the two-dimensional coordinates of the target location underwater using the UUV target spray position. To meet the real-time control requirements and the harsh underwater imaging environment, we proposed a target recognition algorithm to guide the charging platform towards the target direction. The practical underwater alignment experiments demonstrate the method’s strong real-time performance and its adaptability to underwater environments. The final alignment error is approximately 0.5548 mm, meeting the required alignment accuracy and ensuring successful alignment.