This paper presents a fault-tolerant model predictive control approach for cross-rudder autonomous underwater vehicles to achieve heading control, considering rudder stuck faults and unknown disturbances. Specifically, additive faults in the rudders are addressed, and an active fault-tolerant control strategy is employed. Fault models of autonomous underwater vehicles have been established to develop the fault-tolerant control method. In the controller design, the stuck faults of complete rudder failure are incorporated to ensure the heading angle control of the autonomous underwater vehicle in faulty conditions. Furthermore, the fault term is decoupled from the control input, and the decoupled control input, along with corresponding constraints, is incorporated into the model’s predictive controller design. This approach facilitates controller reconfiguration, thereby enhancing and optimizing control performance. Simulation results demonstrate that the proposed fault-tolerant model predictive control method can effectively achieve stable navigation and heading adjustment under rudder fault conditions in autonomous underwater vehicles.

Side-scan sonar (SSS) images present unique challenges to computer vision due to their lower resolution, smaller targets, and fewer features. Although the mainstream backbone networks have shown promising results on traditional vision tasks, they utilize traditional convolution to reduce the dimensionality of feature maps, which may cause information loss for small targets and decrease performance in SSS images. To address this problem, based on the yolov8 network, we proposed a new underwater target detection model based on upsampling and downsampling. Firstly, we introduced a new general downsampling module called shallow robust feature downsampling (SRFD) and a receptive field convolution (RFCAConv) in the backbone network. Thereby multiple feature maps extracted by different downsampling techniques can be fused to create a more robust feature map with a complementary set of features. Additionally, an ultra-lightweight and efficient dynamic upsampling module (Dysample) is introduced to improve the accuracy of the feature pyramid network (FPN) in fusing different levels of features. On the underwater shipwreck dataset, our improved model’s mAP50 increased by 4.4% compared to the baseline model.

This paper presents a Gaussian Process (GP)-based Funnel Model Predictive Control (MPC) for docking control of unmanned underwater vehicles (UUVs). The control method employs a Gaussian Process regression to learn the residual dynamics, which compensates for the unmodeled dynamics to improve prediction accuracy. Furthermore, a distance-adaptive performance funnel is designed to satisfy the field of view (FOV) constraints of sensors during the terminal guidance phase. The funnel imposes time-varying constraints on the UUV to ensure that the docking station remains observable. This funnel constraint is integrated into the cost function of the MPC, which systematically enforces safety without the computational complexity of traditional invariant sets. Comparative simulations validate the framework’s reliability under external disturbances, demonstrating superior tracking precision against conventional MPC benchmarks.