To address the challenges in multi-AUV formation maneuvering, such as limited state perception and transmission capabilities, acoustic communication delays, data loss, and reduced observability due to the lack of position information exchange, this study proposes an event-triggered metrology−communication unified framework with a Lyapunov-based model predictive formation control method (ETMCU−LMPC). The proposed approach aims to enhance formation stability and tracking accuracy.

First, by integrating the formation communication topology with system states, an event-triggered mechanism based on state observation is established. This mechanism leverages relative measurements among AUVs to mitigate delays and data loss caused by acoustic communication failures, while improving system observability in the absence of position information exchange. Second, a distributed model predictive controller based on Lyapunov theory is designed. The controller employs backstepping to construct contractive constraints, ensuring recursive feasibility, and incorporates adaptive Kalman filtering (AKF) to compensate for measurement noise, thereby guaranteeing closed-loop stability.

Simulation results of the formation control for five AUVs (1 leader and 4 followers) show that, compared with the traditional LMPC, the proposed ETMCU−LMPC method reduces the convergence time from 8 s to 6 s, the maximum error from 1.12 m to 0.36 m, and the steady-state error from 0.57 m to 0.06 m. Additionally, the control input exhibits greater stability.

The proposed method can effectively cope with communication anomalies, improve the reliability of multi-AUV formations under scenarios with limited state perception and transmission, and thus possesses practical engineering significance.

To address the challenges of preventing non-random multiple concurrent faults caused by cable aging in shipboard power grids through preventive reconfiguration, and to resolve the issue of unreasonable weight coefficient settings in multi-objective reconfiguration models, thereby enhancing the safety and reconfiguration efficiency of shipboard power grids, a predictive fault reconfiguration method for shipboard power grids based on a double-level optimization strategy is proposed.

A cable aging fault prediction model for shipboard grids was constructed based on Markov chains and thermo-electro-mechanical multi physics analysis. This model was integrated as a constraint into the reconfiguration framework to avoid high-risk branches. A dual-layer optimization strategy was proposed: the upper layer dynamically solves multi-objective weight coefficients using the whale migration algorithm (WMA), while the lower layer determines the optimal switch configuration for grid reconfiguration using a multi-strategy-improved dung beetle optimizer (MSDBO).

After integrating the fault prediction model, the reconfiguration strategy achieved 100% avoidance of high-risk branches (fault probability ≥0.5) proactively. Compared to the conventional two-step passive reconfiguration strategy, convergence speed improved by 47.06%. The dual-layer optimization framework enabled adaptive dynamic adjustment of weight coefficients and increased reconfiguration convergence speed by 56.25%.

The integration of the cable aging fault prediction model and the dual-layer optimization framework effectively enables predictive reconfiguration of shipboard power grids. This approach proactively mitigates non-random faults while significantly improving reconfiguration efficiency and rationality. It offers a novel solution for addressing predictive reconfiguration challenges in non-random multiple-fault scenarios.

This paper addresses the path tracking control problem for underactuated unmanned surface vehicles (USVs) under the conditions of lumped disturbances, input saturation, and limited onboard energy. These factors complicate the path tracking process and reduce the effectiveness of traditional control methods. The aim of this study is to propose an event-triggered fixed-time path tracking control strategy that improves robustness, energy efficiency, and tracking precision in complex environments.

The proposed control strategy integrates several key components to address the challenges mentioned. First, a longitudinal speed guidance law and a fixed-time line-of-sight (SGFTLOS) guidance law are designed to provide the desired longitudinal speed and heading angle for the USV, ensuring it follows the trajectory with optimal speed and heading. Next, to handle model uncertainties and external disturbances (such as wind and current), a Fixed-Time Extended State Observer (FESO) is introduced. The FESO estimates and compensates for lumped disturbances, improving the system's robustness in uncertain environments. To address input saturation, an auxiliary dynamic system is designed to smooth inputs and maintain stable path tracking, even when saturation occurs. Finally, to overcome onboard energy limitations, a periodic event-triggered mechanism based on relative threshold is proposed. This mechanism adjusts control signal update frequency based on system states, minimizing unnecessary actuator activity and energy consumption.

The stability of the system is proven to be fixed-time stable using Lyapunov's fixed-time stability theory, which also eliminates Zeno behavior (infinite triggering in finite time) that could otherwise cause instability. SimuNPS simulation results demonstrate that the tracking error converges within a fixed time, verifying the effectiveness of the proposed method. Compared to existing methods, the proposed strategy exhibits faster transient response, smaller steady-state errors, and superior robustness in the presence of lumped disturbances. Furthermore, the introduction of the FESO provides accurate real-time disturbance estimation, allowing the controller to compensate for disturbances and maintain precise path tracking. Additionally, the event-triggered mechanism significantly reduces the number of control signal updates and actuator actions, improving the system's energy efficiency.

The proposed event-triggered fixed-time path tracking control strategy effectively addresses the challenges of lumped disturbances, input saturation, and limited onboard energy in underactuated USVs. By integrating event-triggered mechanisms, innovative guidance laws, and robust disturbance compensation, the strategy provides a reliable solution for path tracking in complex and uncertain environments. The fixed-time convergence property ensures that the USV achieves desired performance within a fixed time, making the strategy suitable for real-time applications requiring stability, precision, and energy efficiency. This method offers a robust, efficient, and reliable solution for USV path tracking control under difficult operational conditions.

To address the inherent trade-off between large-scale exploration and high-precision manipulation in existing underwater vehicles, a novel morphable underwater intervention robot is developed. Designed for operations at depths of up to 1000 m, the robot integrates low-drag cruising with dual-arm collaborative capabilities, meeting the stringent inspection and maintenance requirements of offshore wind farms and subsea oil and gas platforms.

The overall design specifications were first established, followed by the optimization of the integrated design workflow. The configuration of the robot's pressure-resistant hulls and equipment layout were finalized, with the development of key components, including the morphing mechanism (lead screw lifting mechanism) and pressure-resistant hulls. Strength verification of key components was performed using finite element analysis (FEA) under a 12 MPa hydrostatic load, simulating a depth of 1000 m. Subsequently, the endurance and maneuverability during cruising mode, as well as the manipulator workspace and stability during manipulating mode, were systematically evaluated. Finally, hydrodynamic drag characteristics were verified through CFD simulations, and a coupled vehicle-manipulator dynamic model was developed in Matlab to validate the robot's self-recovery, disturbance rejection, and coupling suppression performance.

The results indicate that the internal layout is rational, with critical components meeting the operational requirements for 1000 m deep-sea environments. The maximum stress within the pressure hulls remains below the yield strength of the selected materials. In cruising mode, the robot achieves a maximum endurance of 7 h, and the configured propulsion system ensures high underwater maneuverability. At a cruise speed of 6 kn, the longitudinal drag is recorded at only 725.06 N, significantly lower than that in manipulating mode, demonstrating superior low-drag characteristics. In manipulating mode, the central buoyancy module is raised by 270 mm, increasing the vertical distance between the center of gravity and the center of buoyancy by 0.054 m. As a result, the maximum restoring moment increases by 202.1% compared to cruising mode, significantly enhancing operational stability. The heeling self-recovery time is reduced from 180 s to 60 s, alongside improved anti-disturbance capabilities. Furthermore, the dual-arm workspace effectively covers the lateral, forward, and downward regions of the vehicle, ensuring an efficient and collaborative operational envelope.

By utilizing autonomous configuration switching, an overall design scheme for a morphable underwater intervention robot with multi-task execution capability was proposed. This design effectively combines low-resistance detection in cruising mode with high-stability operation in manipulating mode, offering an innovative solution for underwater operations in complex deep-sea scenarios.

To address frequent actuator failures caused by complex underwater environments and the inherent characteristics of unmanned underwater vehicles (UUVs), this study investigates a prescribed performance path-following fault-tolerant control scheme for an underactuated UUV subject to ocean current disturbances, model uncertainties, and actuator faults. To ensure safe UUV navigation, a path-following fault-tolerant controller is designed by integrating an improved prescribed performance function with a barrier Lyapunov function, enabling full-state-constrained fault-tolerant control.

A novel predefined-time disturbance observer is developed to estimate the lumped disturbances arising in UUV path-following, including ocean currents, parameter perturbations, unmodeled dynamics, and thrust loss caused by actuator faults. The lumped uncertainties with actuator faults are incorporated into the prescribed performance fault-tolerant controller for compensation, ensuring that all path-following state errors remain within predefined bounds.

Simulation results demonstrate that the position error, attitude angle error, and angular velocity error converge rapidly while strictly satisfying the prescribed safety constraints, achieving a steady-state position error bound of 1 meter and an attitude angle error bound of 0.05 radians. When the actuators suffer up to 80% thrust loss, the disturbance observer rapidly estimates the lumped disturbances, and the controller compensates for the faults within 1 second without significant path-following deviation. The maximum transient error does not exceed 20% of the prescribed limit. These findings validate the strong robustness of the proposed method against actuator faults. By unifying disturbance observation with prescribed performance constraints, the fault-tolerant control structure is simplified, achieving both fast fault response and full-state safety guarantees.

This work provides a universal solution for high-performance UUV navigation in complex underwater environments.

To investigate the acoustic target strength (TS) characteristics of extra-large unmanned underwater vehicles (XLUUVs), this study conducts a systematic analysis of the TS characteristics of the Orca XLUUV in the 1–10 kHz frequency band.

Based on the Orca model, the finite element method is applied to calculate its TS in the 1–3 kHz frequency band, while the planar element method is employed for the 3–10 kHz band. The results are compared with those obtained from the Benchmark model. To provide a more comprehensive evaluation of unmanned underwater vehicle (UUV) stealth performance, the concept of angular detection probability is introduced. Additionally, a scaled model experiment is conducted in a water tank, and a correction method is proposed for the experimental TS measurements.

The TS characteristics of the Orca model are first analyzed. Compared with the Benchmark model, the Orca model exhibits superior stealth performance in the azimuthal direction, along with additional advantages in the circumferential direction at higher frequencies effects that become more pronounced as frequency increases. For experimental cases in which the distance between the hydrophone and transducer does not meet the far-field conditions, the measured TS values are corrected, yielding improved consistency with the simulation results. This validates the accuracy of the numerical simulation results.

The findings of this study provide a theoretical foundation for optimizing underwater detection systems and enhancing the stealth design of UUVs.

To address the low docking accuracy of autonomous underwater vehicles (AUVs) in complex underwater environments, a multi-feature fusion vision-based method is proposed.

A self-developed rudderless vector propulsion AUV with four thrusters was used, and the dark channel prior (DCP) dehazing algorithm was adopted for image enhancement. An improved Canny edge detection algorithm was combined with color threshold segmentation to achieve multi-feature fusion. The minimum enclosing circle method was utilized for circle center positioning, and coordinate transformation was performed to calculate the relative position and orientation for docking.

Unity 3D simulations and pool experiments revealed a distance-dependent trend: both mean difference and root mean square error decreased as docking distance decreased. Closer distances yielded higher visual ranging accuracy and docking precision. When the docking distance was less than 2 m, the positioning error was maintained below 5 cm, with an overall success rate of 88%.

The proposed method fulfills the accuracy requirements for AUV autonomous docking and provides a highly robust solution for underwater equipment recovery.

With the increasing diversification of application requirements for unmanned underwater vehicles (UUVs), traditional design methods centered on text-based documentation have revealed numerous limitations in practice, such as scattered design documents, difficulty in maintenance, and low iteration efficiency among systems. Therefore, it is necessary to introduce a novel overall design methodology.

In this study, the model-based systems engineering (MBSE) methodology was incorporated into the design process of UUVs and integrated with traditional design approaches to establish a model-driven design and verification framework. Using the M-Design collaborative research platform, graphical system modeling language (SysML) was employed to construct a comprehensive system model, including the requirements model, logical architecture model, and physical architecture model, thereby forming an integrated design framework. To further validate the feasibility of the framework, multi-system co-simulation technology was adopted, and a distributed simulation platform was developed to perform performance simulation and verification for typical mission scenarios of UUVs. Based on these efforts, a conceptual scheme for an agile design and verification prototype system has been proposed to support agile development requirements.

The results demonstrate that the model-based design methodology can significantly improve the design efficiency and verification capability of UUVs, enabling a closed-loop development process from requirements definition to design implementation.

The proposed design methodology provides effective guidance for the design and specification verification of various manned and unmanned underwater platforms.