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.

A new control algorithm was designed to solve the problems of actuator physical failure, remote network attack, and sudden change in trajectory curvature when a port’s artificial intelligence-based transportation robots track transportation in a freight yard. First of all, the nonlinear, redundant, saturated sliding surface was designed based on the redundant information of sliding mode control caused by the finite nature of control performance; the dynamic acceleration characteristic of super-twisted sliding mode reaching law was considered to optimize the control high frequency change caused by trajectory mutation; and an improved super-twist reaching law was designed. Then, a nonlinear factor was designed to construct a nonlinear, fault-tolerant filtering mechanism to compensate for the abnormal part of the unknown input that cannot be executed by adaptive neural network reconstruction. On this basis, the finite-time technology and parameter-event-triggered mechanism were combined to reduce the dependence on communication resources. As a result, the design underwent simulation verification to verify its effectiveness and superiority. In the comparative simulation, under a consistent probability of a network attack, the tracking accuracy of the algorithm proposed in this paper was $22.65\%$, $12.69\%$ and $11.48\%$ higher those that of the traditional algorithms.