The Autonomous Underwater Vehicle’s body attitude has a great influence on some specific underwater tasks, such as topographic prospecting, target detection, etc. Therefore, this paper investigates an improved integral sliding mode control (IISMC)-based attitude controller for AUV with model uncertainties and external disturbances to improve the ability of attitude tracking for AUV. To reduce the influence of strong interference on the integral term, the Gaussian function is introduced in integral sliding mode controller. Moreover, the Lyapunov function is used to prove the stability of IISMC-based attitude control law. Finally, the numerical simulations on MATLAB/Simulink are provided to demonstrate the proposed IISMC has smaller tracking error and converges faster than Sliding Mode Control (SMC) and Integral Sliding Mode Control (ISMC)-based attitude-control laws under different disturbances. Better yet, the effectiveness of the proposed IISMC-based attitude control law is tested in field experiments.

As we all know, target detection and tracking are of great significance for marine exploration and protection. In this paper, we propose one Convolutional-Neural-Network-based target detection method named YOLO-Softer NMS for long-strip target detection on the water, which combines You Only Look Once (YOLO) and Softer NMS algorithms to improve detection accuracy. The traditional YOLO network structure is improved, the prediction scale is increased from threeto four, and a softer NMS strategy is used to select the original output of the original YOLO method. The performance improvement is compared totheFaster-RCNN algorithm and traditional YOLO methodin both mAP and speed, and the proposed YOLO–Softer NMS’s mAP reaches 97.09%while still maintaining the same speed as YOLOv3. In addition, the camera imaging model is used to obtain accurate target coordinate information for target tracking. Finally, using the dicyclic loop PID control diagram, the Autonomous Surface Vehicle is controlled to approach the long-strip target with near-optimal path design. The actual test results verify that our long-strip target detection and tracking method can achieve gratifying long-strip target detection and tracking results.

Path tracking control is an important method for a Six-Degree-Of-Freedom Autonomous Underwater Vehicle to perform specific underwater tasks. Therefore, this paper investigates a dual-loop integral sliding mode control (DLISMC)-based tracking controller for an AUV with model uncertainties and external disturbances, and introduces a new reaction torque model for static compensation in order to improve the attitude control capability for AUVs when performing path tracking. In addition, the stability of tracking control law based on DLISMC is demonstrated using the Lyapunov function. Finally, numerical simulations are carried out on MATLAB 2016/Simulink and compared with the Proportion–Integral–Differential (PID) control commonly used in industry as well as the dual-loop Proportion–Integral–Differential (DLPID). Simulation results show that the DLISMC has a smaller tracking error, faster convergence speed, and more robustness against external disturbances and reaction torque.