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.

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guidance law ensures that when the vessel enters the coverage area of the boundary circle, the guidance signal is no longer updated, thus preventing unnecessary signal transmission and conserving communication resources.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.