This paper investigates the formation keeping problem of heterogeneous ships with underactuated inputs, uncertain dynamics, and environmental disturbances. The control objective is to make the heterogeneous followers keep the desired formation while tracking a leader. To solve the problem effectively, a novel virtual leader–follower formation scheme considering the ship heterogeneity is proposed by utilizing the backstepping method, adaptive neural network, and adaptive control law. The stability of the formation control system is proved based on Lyapunov's direct method where all tracking errors are guaranteed to be uniformly ultimately bounded. Finally, simulations and comparisons are conducted to verify the effectiveness of the proposed control law.

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Autonomous docking and undocking control is an important part of intelligent ship motion control. In this study, the adaptive-mutation beetle swarm prediction (AMBS-P) algorithm is used to propose a control approach for autonomous docking and undocking. Firstly, this paper introduces the principle of the AMBS-P algorithm, then the convergence is proved. Secondly, the “Tito-Neri” model ship is introduced as a case study, and the thrust allocation process is described. Finally, the effect of docking and undocking is verified in multiple scenarios starting from different angles. In the verification, first of all, when designing the docking and undocking controllers, the correctness of the algorithm and the practicality of the control are verified by whether there is ship drag or not. Secondly, by analyzing the parameters of the algorithm, the optimal parameters of it are determined and verified in the real environment. Thirdly, compared with typical proportion–integral–derivative (PID) algorithm and nonlinear model predictive control (NMPC) algorithm, the AMBS-P algorithm has better results for autonomous docking and undocking control, no matter in long-distance or short-distance. The research shows that the AMBS-P algorithm has a fast response and good effect for the ship autonomous docking and undocking, and does not rely too much on the system model.

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Autonomous shipping refers to the ability of a ship to independently control its own actions while transporting cargo from one port to another, which places higher requirements on ship motion control methods. When a ship enters a port, it is important to ensure that the ship sails from the fairway area to the assigned position at the berth with a desirable speed and that it finally stops at the desired position. Ship docking is known as one of the most challenging tasks due to the non-linearity of low-speed ship movements and the high requirements on collision avoidance with the quayside. This paper proposes a nonlinear model predictive control (NMPC)-based approach for underactuated ships, providing optimal ship rudder angles and propeller revolution rate to automate the ship docking process. At each sampling instant, a finite horizon optimal control problem is formulated based on a nonlinear ship maneuverability model. A lexicographic multi-objective optimization strategy is proposed in the design of the NMPC controller, saving the efforts on control parameters tuning. Simulation experiments are carried out to evaluate the effectiveness of the proposed approach.

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The future autonomous ships will be operating in an environment where different autonomous and non-autonomous vessels with different characteristics exist. These vessels are owned by different parties and each uses its owned unique approaches for guidance and navigation. The Collaborative Autonomous Shipping Experiment (CASE) aims at emulating such an environment and also stimulating the move of automatic ship control algorithms towards practice by bringing together different institutes researching on autonomous vessels under an umbrella to experiment with collective sailing in inland waterways. In this paper, the experiments of CASE 2020 are explained, the characteristics of different participating vessels are discussed and some of the control and perception algorithms that are planned to be used at CASE 2020 are presented. CASE 2020 will be held in parallel to iSCSS 2020 at Delft University of Technology, the Netherlands.@en

Intelligent motion control is one of the key technologies of ships. This paper studies the application of Adaptive Mutation Beetle Particle Swarm (AMBPS)-PID algorithm in ship motion control. Firstly, the ship MMG model is established. Then the BAS algorithm is introduced, and AMBPS algorithm is improved and designed on this basis. Secondly, ship heading and path following controllers are designed according to the algorithm, and rudder turning rate constraint is introduced to limit the rudder angle. Thirdly, through the test function effect analysis of AMBPS and other similar algorithms, the improved effect of this algorithm is verified. Finally, from manual tuning PID parameters to off-line and on-line optimizing parameters based on AMBPS algorithm, the optimal control parameters are obtained step by step, and the optimal heading and path following simulation results are achieved. Compared with the results of traditional PID, AMBPS-PID algorithm has a better adaptive control effect on ship motion control, reduces the error of manual tuning parameters and improves efficiency.@en

Ship collision is the main type of maritime accidents, which causes great losses on human lives and economy, and brings negative impacts to the maritime environment. In crowded waters such as the sea area near a seaport, multiple ships encountering situations happen frequently. While several methods have been proposed for solving multiple ships collision avoidance problem, most research focuses on the safety guarantee and time-availability of anti-collision decisions, and less attention is paid on improving the efficiency of collision avoidance maneuvers. This paper proposes a rolling horizon optimization approach for multiple ships from a global optimal perspective, with the aim to minimize the time costs and course angle alterations of the anti-collision operations. A ship maneuverability model is used to make predictions and calculations of inter-ship collision risks, upon which an overall optimization problem is formulated. Continuous time is divided into a set of discrete time slots, and an iterative optimization procedure is carried out to determine the optimal course angles for the ships at each time slot. A PID heading controller is designed to implement the optimal course angles. When collision risks among ships no longer exist, the optimization procedure terminates. To evaluate the performance of the proposed approach, simulation experiments regarding 7-ships and 12-ships encounter situations are carried out.

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At present, with the development of waterborne transport vehicles, research on ship faces a new round of challenges in terms of intelligence and autonomy. The concept of maritime autonomous surface ships (MASS) has been put forward by the International Maritime Organization in 2017, in which MASS become the new focus of the waterborne transportation industry. This paper elaborates on the state-of-the-art research on motion control of MASS. Firstly, the characteristics and current research status of unmanned surface vessels in MASS and conventional ships are summarized, and the system composition of MASS is analyzed. In order to better realize the self-adaptability of the MASS motion control, the theory and algorithm of ship motion control-related systems are emphatically analyzed under the condition of classifying ship motion control. Especially, the application of intelligent algorithms in the ship control field is summarized and analyzed. Finally, this paper summarizes the challenges faced by MASS in the model establishment, motion control algorithms, and real ship experiments, and proposes the composition of MASS motion control system based on variable autonomous control strategy. Future researches on the accuracy and diversity of developments and applications to MASS motion control are suggested.@en