A high-precision iterative control strategy based on automatic identification system (AIS) trajectory reproduction tasks is designed to address the low tracking accuracy, system model uncertainties, and disturbances from time-varying marine environments encountered by underactuated ships.

First, a guidance method driven by AIS data is proposed, integrating AIS data with the dynamic virtual ship (DVS) guidance principle. Second, robust neural damping technology and dynamic surface control (DSC) are introduced to approximate model uncertainties and complex derivatives of virtual control laws, thus avoiding the issue of “computational explosion”. Furthermore, an iterative learning control strategy is incorporated based on traditional robust control methods to complete the final controller design. Finally, the semi-global uniformly ultimately bounded (SGUUB) stability of the proposed controller is demonstrated using the Lyapunov theorem. A simulation experiment is conducted to validate its performance under time-varying marine environmental disturbances.

The results indicate that the path following of underactuated ships under this control strategy exhibits high precision and robustness, with average control accuracy maintained within 0.5 meters.

The research results are of great significance for safe ship navigation and trajectory reproduction in complex waters.

To meet the demands for high-precision path tracking and communication resource optimization in unmanned surface vehicle (USV) formations navigating in non-cooperative target tracking scenarios, this paper proposes a novel prescribed performance formation control algorithm integrated with a dynamic event-triggered mechanism (DETM).

First, an adaptive guidance switching framework based on a virtual leader-follower structure is developed. During waypoint-based synchronous cruising, the formation structure is maintained by fixing relative distances and bearings. When facing a non-cooperative target vessel, decoupling conditions for formation reference signals are introduced, along with a velocity correction mechanism and an adaptive law to estimate the target vessel’s motion characteristics, thereby ensuring autonomous reconfiguration capability. Second, to overcome the strict sign constraints of boundary functions in traditional prescribed performance control (PPC) during initial error stabilization, a novel set of performance boundary functions is proposed to confine arbitrary initial errors within designated boundaries. Through a mapping transformation, the tracking error constraint problem is transformed into a stabilization problem of new variables, thereby ensuring that the formation’s position and heading errors remain strictly within the prescribed performance limits. Additionally, a dual-threshold regulated DETM is introduced to effectively reduce communication load between controllers and actuators. Unlike static mechanisms, the dynamic mechanism enables online adaptive updates of time-varying thresholds based on system states, while theoretically eliminating Zeno behavior. Meanwhile, radial basis function neural networks (RBF-NNs) are employed to approximate inherent nonlinearities in the model and unknown external disturbances, and are combined with dynamic surface control (DSC) to avoid repeated differentiation of virtual control laws. Based on Lyapunov stability theory, the semi-globally uniformly ultimately bounded (SGUUB) stability of the closed-loop system is proven. Finally, two simulation experiments quantitatively validate the algorithm.

The results show that the proposed algorithm achieves high precision, strong robustness, and low communication load during path tracking, with an average position error below 0.5 meters and a heading error within 5 degrees. Compared to static event triggering, the DETM further reduces control update frequency by 8.79% for surge moment and 11.81% for yaw moment while maintaining tracking stability. The framework successfully demonstrates complete processes including cooperative cruising, autonomous target eviction, dynamic decoupling, and reconfiguration, achieving optimized control accuracy and efficient use of communication resources.

The proposed dynamic event-triggered prescribed performance formation control framework integrates velocity correction with adaptive compensation, enabling high-accuracy tracking and enhanced communication efficiency in non-cooperative scenarios. This approach provides new theoretical and technical foundations for non-cooperative control and adaptive reorganization in unmanned surface vehicle formation systems.

Amid energy and environmental challenges, sail-assisted ships are key to low-carbon shipping. Marine disturbances and communication limits degrade their path-following performance. This work proposes an adaptive tracking algorithm for rotor-sail ships. Utilizing the Magnus effect, it achieves high propulsion efficiency, simple structure and good adaptability.

First, a modified guidance law is constructed by improving the traditional logic virtual ship (LVS) guidance principle. This improvement involves the incorporation of an intervention method based on a finite boundary circle, effectively reducing the communication load of the guidance system. The modified guidance law ensures that when the vessel enters the coverage area of the boundary circle, the guidance signal is no longer updated, thus preventing unnecessary signal transmission and conserving communication resources. Meanwhile, to address the issue of actuator input saturation, a saturation compensation function is integrated into the guidance law, which helps to ensure that the system remains within operational limits of the actuators, thus enhancing the robustness of the control system. Secondly, radial basis function (RBF) neural networks are employed for online approximation of system uncertainties. The RBF neural networks can respond in real time to changing dynamic conditions, thereby providing an effective mechanism to compensate for unmodeled dynamics or external disturbances that may affect the vessel's tracking trajectory. To avoid the "explosion of computational complexity" inherent in traditional backstepping control, dynamic surface control (DSC) technique is introduced. This technique simplifies the control law by using first-order filters, which significantly reduces the computational burden and prevents the growth of intermediate variables that would otherwise increase computational complexity. Furthermore, a robust adaptive control algorithm is designed by combining neural damping and adaptive techniques. This is coupled with an integral event-triggered mechanism, which is particularly important in dealing with slight fluctuations in system states. Traditional event-triggered mechanisms, which rely on instantaneous state measurements, may fail to trigger updates in cases of minor state fluctuations, leading to long periods without signal updates, thus degrading the system’s closed-loop performance. The proposed integral event-triggered mechanism can effectively avoid long periods of non-triggering caused by minor state fluctuations. Its triggering effect is more natural and efficient, thus significantly reducing the frequent transmission of control commands and mechanical wear of actuators. Finally, the stability of the proposed control algorithm is rigorously analyzed using Lyapunov theory to guarantee that all error signals are semi-global uniform and ultimately bounded (SGUUB). To validate the proposed control strategy, numerical simulations are conducted in MATLAB, where marine environmental disturbance under a sea state level of 4 is simulated based on the NORSOK wind spectrum and the JONSWAP wave spectrum.

The results of simulations demonstrate that the proposed algorithm significantly enhances the path following performance of sail-assisted vehicles. The proposed algorithm exhibits high control accuracy and fast response, maintaining the position and heading errors within ranges of 3 meters and 5 degrees, respectively. Notably, due to the introduction of the event-triggered mechanism and servo systems, the control inputs remain within the allowable range of actuator operations and signal chattering is significantly reduced, effectively minimizing mechanical wear on actuators. Additionally, the adaptive laws embedded in the control algorithm demonstrate effective convergence, ensuring that the system can reach a stable operating condition despite dynamic disturbances present in the marine environment. The utilization of the proposed sail-assisted navigation strategy can achieve an 11.6% improvement in propulsion efficiency under a sea state level of 4, substantially reducing energy consumption and promoting sustainable maritime operations.

The path following performance of the proposed system exhibits not only low communication load but also strong robustness, making it suitable for practical deployment in maritime navigation. The findings provide a practical and feasible technical pathway for green transformation of marine vessels, contributing to development of more sustainable and energy-efficient shipping technologies. Therefore, the proposed control algorithm and sail-assisted strategy could play a vital role in advancing future of green maritime transportation.

To solve the problems of communication resource limitations and parameter uncertainty in the dynamic positioning control tasks of fully driven ships in marine engineering applications, this paper presents a robust event-triggered control algorithm for ship dynamic positioning that considers the dynamic characteristics of the actuators.

The algorithm uses a radial basis function (RBF) neural network to approximate system uncertainty. At the same time, a novel event-triggering mechanism in the sensor-controller channel is designed by introducing a zero-order hold which reduces the signal transmission frequency in the sensor−controller and controller−actuator channels, thus greatly saving the communication resources of the system. In addition, the adaptive parameters are updated online and designed to compensate for the gain uncertainty of the actuators, which reduces the computational load and ensures that the ship can perform dynamic positioning tasks stably.

The Lyapunov stability theory is used to prove that all error variables in the closed-loop control system satisfy semi-global uniformly ultimately bounded (SGUUB) stability, and the effectiveness of the proposed algorithm is verified through comparison with a simulation.

The results of this study can provide useful references for promoting the development of intelligent ship equipment.

A robust adaptive course-keeping control algorithm is designed to deal with the course-keeping problem for under-actuated ships with rudder faults, gain uncertainty and marine disturbances.

By combining the robust neural damping technique and adaptive approach, numerous neural network (NN) weights can be compressed horizontally, and only two gain-related adaptive learning parameters need to be designed to compensate for both the gain uncertainty and unknown fault parameters. The proposed controller is proven to be semi-global uniform and ultimately bounded (SGUUB) through Lyapunov analysis. Finally, the Nomoto mathematical model is established using "Yukun", and the effectiveness and superiority of the course-keeping algorithm is illustrated by carrying out comparison experiments under marine interference conditions.

The results show that the average rudder angle of "Yukun" under rudder failure is reduced by 51%, significantly improving control performance.

The results of this study can provide references for tackling the course-keeping control problem of under-actuated ships.