Baigang Zhao

In order to realize waypoint-based automatic berthing alongside or astern, a generation algorithm of dynamic virtual ships is proposed to guide the underactuated surface vehicle (USV). In this paper, a nonlinear model predictive control (NMPC) problem is set up with multiple shooting method, which lifts the system and improves convergency. Measuring error and disturbances are reduced by moving horizon estimation (MHE) where the weights of sensors are auto-tuned according to their variation. Simulations of berthing alongside and astern of ship BAY CLASS illustrates the position error is smaller than 0.6 m and course error is smaller than 0.5°. Compared to adaptive sliding mode control, proposed method has advantage on state smoothness and the control performance meets the requirements of berthing accuracy.

In order to further explore more efficient identification algorithms that can solve the ship motion identification modeling problem, a novel identification algorithm for 3-DOF ship maneuvering modeling is proposed in this paper. Based on the recursive least-squares method, the proposed algorithm combines multi-innovation and nonlinear innovation techniques that focus on the innovation’s processing. In addition, its convergence capability is theoretically demonstrated. The identification algorithm shows good accuracy and convergence that is verified by a simulation experiment on a container ship. Furthermore, another simulation experiment using the full mission ship handling simulator demonstrates its generalization capability.

Ocean-going vessels usually sail at a constant speed in open sea. Rule 8 of the International Regulations for Preventing Collisions at Sea (COLREGs) recommends alteration of course as the single most effective way to avoid collisions. This makes the study of constant speed collision avoidance (CSCA) critical for the development of automatic navigation systems for ocean-going vessels. This paper presents a 4-DOF MMG ship motion mathematical model that can capture the maneuvering characteristics of a ship using collision avoidance (CA). Drawing upon a velocity obstacle (VO) method, a velocity-derived heading obstacle algorithm (VHO) that meets COLREGs (Rule 8, 13, 14 and 15) constraints is proposed. The algorithm does not control the speed of the ship, but rather guides its course to prevent collisions. To aid determination of when CA action should be employed, a new function is also defined. CSCA simulations for scenarios involving both two ships and multiple ships were designed and carried out. In simulations, all ships could avoid other ships, keeping a distance of more than 1 nautical mile from each other, which demonstrates that the VHO algorithm ensures that ships can always keep a safe distance from other ships.

This paper investigates the problem of parameter identification for ship nonlinear Nomoto model with small test data, a nonlinear innovation-based identification algorithm is presented by embedding sigmoid function in the stochastic gradient algorithm. To demonstrate the validity of the algorithm, an identification test is carried out on the ship ‘SWAN’ with only 26 sets of test data. Furthermore, the identification effects of the least squares algorithm, original stochastic gradient algorithm and the improved stochastic gradient algorithm based on nonlinear innovation are compared. Generally, the stochastic gradient algorithm is not suitable for the condition of small test data. The simulation results indicate that the improved stochastic gradient algorithm with sigmoid function greatly increases its accuracy of parameter identification and has 14.2% up compared with the least squares algorithm. Then the effectiveness of the algorithm is verified by another identification test on the ship ‘Galaxy’, the accuracy of parameter identification can reach more than 95% which can be used in ship motion simulation and controller design. The proposed algorithm has advantages of the small test data, fast speed and high accuracy of identification, which can be extended to other parameter identification systems with less sample data.

In this work we propose a solution to the Unmanned Surface Vessels (USV) path-following problem by suggesting an improved model predictive control (MPC) based on global course constraint (GCC) and event-triggered mechanism (ETM). The designed GCC guidance law incorporates the LOS concept and overcomes its lack of reference signal near the waypoint. The developed MPC controller involves an ETM that ultimately optimizes the deviation according to its prediction ability and effect on energy saving. We challenge our model against current methods on the evaluation platform vessel “Yukun” of Dalian Maritime University and demonstrate the effectiveness and superiority of the suggested path-following algorithm over existing methods.

To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.