Search Results (5)

To ensure the safe navigation of ships in rough seas while reducing steering gear energy consumption and losses, a steering control system with small rudder output angles, low steering frequency, and high control performance was designed. A third-order closed-loop gain-shaping algorithm was employed in the development of the controller, with the ultimate control strategy derived by embedding a nonlinear compound function between the proportional derivative (PD) controller and the second-order oscillation link to enhance control effectiveness. A nonlinear Nomoto model of the “Yupeng” ship was employed for simulation validation. The simulation results illustrated a 14.5% improvement in overall control performance achieved by the proposed controller compared to a nonlinear feedback controller. The controller’s robustness was additionally validated through the application of the Norrbin ship model. The proposed controller enhances the stability of ships in rough seas, effectively limiting the maximum rudder angle during turns and reducing the average rudder angle and steering frequency during navigation. This design aligns with practical requirements for maritime operations in heavy weather, contributing significantly to the economic, safe, and efficient navigation of ships. Full article

The accurate nonlinear modeling of an unmanned surface vessel (USV) is essential for advanced control and operational performance. This paper combines the locally weighted regression (LWR) algorithm and the extended Kalman filter (EKF) for parameter identification using state data from full-scale vessel experiments. To mitigate the effects of disturbances and abrupt changes in the full-scale vessel data, LWR filtering is applied for data smoothing before parameter identification. The EKF is then used to estimate the unknown parameters in the second-order nonlinear Nomoto model of the USV. These parameters are incorporated into the Nomoto model, and simulations are conducted by inputting the same rudder inputs as in the experimental data. The predicted heading angle and yaw rate are compared with experimental results, showing that the mean absolute error (MAE) for the heading angle is within 10° and the MAE for the yaw rate is within 1.5°/s. Additionally, the coefficient of determination (R²) values for both predictions are above 0.93. The simulation results demonstrate that the combination of LWR filtering and EKF effectively identifies parameters and models the nonlinear response of the USV, achieving high accuracy in the established second-order model. Full article

Enhancing the path planning capabilities of ships is crucial for ensuring navigation safety, saving time, and reducing energy consumption in complex maritime environments. Traditional methods, reliant on static algorithms and singular models, are frequently limited by the physical constraints of ships, such as turning radius, and struggle to adapt to the maritime environment’s variability and emergencies. The development of reinforcement learning has introduced new methods and perspectives to path planning by addressing complex environments, achieving multi-objective optimization, and enhancing autonomous learning and adaptability, significantly improving the performance and application scope. In this study, we introduce a two-stage path planning approach for large ships named MAPF–DQN, combining Manipulation-Compliant Artificial Potential Field (MAPF) with Deep Q-Network (DQN). In the first stage, we improve the reward function in DQN by integrating the artificial potential field method and use a time-varying greedy algorithm to search for paths. In the second stage, we use the nonlinear Nomoto model for path smoothing to enhance maneuverability. To validate the performance and effectiveness of the algorithm, we conducted extensive experiments using the model of “Yupeng” ship. Case studies and experimental results demonstrate that the MAPF–DQN algorithm can find paths that closely match the actual trajectory under normal environmental conditions and U-shaped obstacles. In summary, the MAPF–DQN algorithm not only enhances the efficiency of path planning for large ships, but also finds relatively safe and maneuverable routes, which are of great significance for maritime activities. Full article

Significant changes in the load of cargo ships make it difficult to simulate and control their motion. In this work, a parameter prediction method for a ship maneuvering motion model is developed based on parameter identification and support vector regression (SVR). First, the effects of least-squares (LS) and multi-innovation least-squares (MILS) parameter identification methods for the non-linear Nomoto model are investigated. The MILS method is then used to identify the parameters of the non-linear Nomoto model under various load conditions, and model training datasets are established. On this basis, SVR is used to predict the parameters of the non-linear Nomoto model. The results reveal that the MILS method converges faster than the LS method. The SVR method achieves lower accuracy than the MILS method, but exhibits reasonable prediction accuracy for zigzag motions, and the maneuvering motion model can be predicted as navigation conditions change. Full article

Most of the existing nonlinear ship course-keeping control systems are designed with the Nomoto model, which solely considers the yawing of the ship with only one Degree of Freedom (DOF), and it does not consider the coupling between the longitudinal and the lateral velocity of the ship. In this paper, a nonlinear ship course controller design method that can be used in a nonlinear coupled model was proposed. A stable nonlinear ship course controller with anti-wind and anti-wave interference was constructed based on the Lyapunov stability principle and robust control theory, which can be used in the course control of autopilot in the case of wind and waves. In this method, the coupling among the longitudinal and lateral velocity as well as yawing of the ship was considered. The simulation results showed that the method can not only effectively control the ship’s course but also can track the dynamic course effectively. At the same time, compared with the PID control method based on backstepping, the steering angle of the rudder angle of our method is smaller and the wear and tear of steering gear will be smaller. Full article