Yancai Hu

Unmanned surface vehicles (USVs) are increasingly critical in modern maritime operations, where reliable cooperative formation control under actuator failure is essential for safe navigation and efficient mission execution. Thus, this study presents an innovative fault-tolerant control strategy for USV formations, specifically addressing the challenges posed by actuator degradation, compound uncertainties, and input saturation. Concretely, the main contribution of this study is as follows. First, a detailed analysis of the USV kinematics and dynamics is conducted, and a novel position constraint model is developed through a formation transformation approach. To mitigate internal and external disturbances, a new non-singular terminal sliding mode surface is designed in conjunction with a dynamically regulated convergence law, ensuring finite-time convergence while reducing chattering. An adaptive terminal sliding mode controller is then formulated, integrating an event-triggered mechanism and an RBF neural network to compensate for model uncertainties and input saturation effects. Simulation results demonstrate that the proposed method not only achieves robust cooperative formation control under partial actuator failure but also significantly enhances the tracking accuracy and reduces the communication load compared to conventional sliding mode approaches.

In this paper, a nonlinear sliding-mode super-twisting reaching law algorithm is designed to address the problem of coupling interference under deception attacks and actuator physical faults in USV formations during cooperative mining operations of a USVs-ROVs system. First, a USV model with attacks and disturbances is established, and a leader–follower formation system is designed. Then, based on the reaching law, the state error dynamic chatter can be effectively solved when it is far away from and reaches the sliding surface; a nonlinear sliding super-twisting reaching law is designed to improve the chatter characteristics of the sliding surface. Furthermore, to solve the problems of low fitting accuracy regarding control anomaly information and the difficulty of fending off signal-data interference attacks, a nonlinear saturation fault-tolerant filtering mechanism and a nonlinear fitting factor are designed. Finally, the stability of the algorithm is verified through Lyapunov theory. Under the same coupling deception probability, the nonlinear sliding-mode super-twisting reaching law algorithm designed in this paper enables the leader ship and each follower ship to reach stability within about 12s, and the formation system maintains its formation while also improving the control accuracy of each individual ship.

In multi-vessel collision avoidance decision-making, the collision between merchant and fishing vessels is a significant challenge. This paper proposes a fuzzy fusion method for making avoidance decisions under the influence of the navigation environment. First, C-means clustering was used to collect and analyze Automatic Identification System (AIS) data from fishing vessels. On this basis, the environment collision risk was determined using fuzzy reasoning. Second, the basic collision risk is obtained by calculating the DCPA and TCPA, and the integrated Collision Risk Index (CRI) is concluded by fuzzy logic through basic collision risk and the environment collision risk. The similar cases are extracted from the fuzzy case database, and collision avoidance decisions for merchant vessels are formulated following fuzzy adjustments. Finally, to validate the method, data from Chengshantou coastal waters is employed for verification. The results show that it can provide theoretical guidance and practical value for merchant vessels in making collision avoidance decisions.

Due to the complex maritime navigation environment, Unmanned Surface Vessels (USVs) are influenced by unknown nonlinear dynamics arising from external disturbances and internal uncertainties. Achieving effective formation control while maintaining obstacle avoidance performance presents significant challenges. This article proposes a Neural Networks (NNs) adaptive formation Artificial Potential Field (APF) obstacle avoidance control method for multiple USVs. By employing online updates of Radial Basis Function (RBF) NNs technology, the unknown nonlinear dynamics are approximated, thus addressing complex nonlinear dynamics problems. In scenarios involving multiple USVs navigating under high wind and wave conditions, collisions with obstacles frequently occur. To tackle this issue, a leader-follower control strategy is designed that effectively addresses risk assessment and obstacle avoidance under such challenging conditions. Additionally, to account for saturation constraints or potential faults in the controller inputs commonly encountered in engineering applications, it implements an asymmetric auxiliary control system. Furthermore, the Lyapunov stability theorem is utilized to ensure the stability of both the formation control and obstacle avoidance algorithms for multiple USVs. Finally, the effectiveness of the proposed algorithm is validated through simulations.