Ship Motion Control Research Articles

Review of Ship Behavior Characteristics in Mixed Waterborne Traffic

Through the continuous development of intellectualization, considering the lifecycle of ships, the future of a waterborne traffic system is bound to be a mixed scenario where intelligent ships of different autonomy levels co-exist, i.e., mixed waterborne traffic. According to the three modules of ships’ perception, decision-making, and execution, the roles of humans and machines under different autonomy levels are analyzed. This paper analyzes and summarizes the intelligent algorithms related to the three modules proposed in the last five years. Starting from the characteristics of the algorithms, the behavior characteristics of ships with different autonomous levels are analyzed. The results show that in terms of information perception, relying on the information perception techniques and risk analysis methods, the ship situation can be judged, and the collision risk is evaluated. The risk can be expressed in two forms, being graphical and numerical. The graphical images intuitively present the risk level, while the numerical results are easier to apply into the control link of ships. In the future, it could be considered to establish a risk perception system with digital and visual integration, which will be more efficient and accurate in risk identification. With respect to intelligent decision-making, currently, unmanned ships mostly use intelligent algorithms to make decisions and tend to achieve both safe and efficient collision avoidance goals in a high-complexity manner. Finally, regarding execution, the advanced power control devices could improve the ship’s maneuverability, and the motion control algorithms help to achieve the real-time control of the ship’s motion state, so as to further improve the speed and accuracy of ship motion control. With the upgrading of the autonomy level, the ship’s behavior develops in a safer, more efficient, and more environment-friendly manner.

Data-driven model identification and predictive control for path-following of underactuated ships with unknown dynamics

With the trends towards autonomous shipping, advanced ship motion control methods have received increased attention in recent years. The validity of ship models is crucial in designing motion controllers and directly affects their performances. However, accurate models that could reflect true ship dynamics are highly nonlinear, complex and complicated to identify, especially in situations when the experimental conditions are limited. This paper proposes a data-driven predictive control method for path-following of under-actuated cargo ships with unknown dynamics, which makes use of data gathered during operation to improve the model and the path-following performance. Based on the ship navigation data set, the relations between the heading angle and the rudder angle of the ship are fitted with seven typical regression algorithms, which acts as the prediction model in the path-following controller. Simulation study is carried out to choose the most suitable regression algorithm, among which elastic net regression is selected. The Antenna Mutation Beetle Swarm Predictive (AMBS-P) algorithm is introduced to find the optimal weights in the model identification process. A Line-of-Sight (LOS) algorithm is used as the guidance law to transform reference way-points into reference heading angles, and the path-following controller is designed also based on and the AMBS-P algorithm. Simulation results show that the proposed data-driven control method performs well in the path-following task without having prior knowledge regarding the hydrodynamic coefficients and ship parameters. • The proposed data-driven control strategy allows the users to exploit the advantage of the predictive control scheme, as well as exploiting the approximate model knowledge captured from data without having prior knowledge regarding the hydrodynamic coefficients and ship parameters. • A quantitative analysis of the model identification performances of seven typical regression algorithms is carried out. • The Antenna Mutation Beetle Swarm Predictive (AMBS-P) algorithm is proposed and applied in finding the optimal weights in the model identification process and in designing the path-following controller with a LOS guidance principle. This could save the efforts spend in weight selection and controller parameter adjustments. It is also fast and easy to implement, which could facilitate its use in practice.

DETERMINISTIC-BASED SHIP ANTI-COLLISION ROUTE OPTIMIZATION WITH WEB-BASED APPLICATION

Most of the accidents are caused by human error at sea so, decision making process made by navigators should be more computerised and automated. The supported decision making can be a step forward to decrease the risk of collision. This paper, in this respect, aims to present a deterministic approach to support optimum collision avoidance trajectory. This approach involves a collision avoidance course alteration. A web-based application coded with "JavaScript" programming language on the "Processing" software platform which allows the own ship to change her course in a deterministic manner to avoid collision optimally has been introduced. Algorithm structure of the method has been formulated and organized according to the International Regulation for Preventing Collision at Sea (COLREGs). The experimental tests results have revealed that the system is practicable and feasible and considerably outperforms heuristic-based method. It is thought that the developed method can be applied in an intelligent avoidance system on board and provides contribution to ship collision avoidance process, automation of ship motion control and ship traffic engineering.

Multi‐verse optimizer based PI‐type active disturbance rejection generalized predictive control parameter tuning for ship motion

AbstractThe motion control of ships is the crucial of the transportation industry. The ship is an under‐actuated system with large inertia and nonlinearity, it also will be influenced by various interference during the voyaging. This work proposed a multi‐verse optimizer (MVO) tuned PI‐type active disturbance rejection generalized predictive control (PI‐ADRGPC) method. The PI‐ADRGPC is used to control the ship motion, and the MVO is employed to tune the parameters of PI‐ADRGPC. To verify the performance of the proposed method, a set of simulations with different interference are tested. The results indicate that compared with particle swarm optimization, dragonfly algorithm and moth‐flame optimization, the MVO tuned PI‐ADRGPC has better performance under different interference in the ship motion control. The Monte Carlo test also indicates that the robustness of the system is strong.

An online identification approach for a nonlinear ship motion model based on a receding horizon

The ship motion system is a nonlinear control object, and its system parameters exhibit time-varying characteristics with the ship motion state, which increases the difficulty of control. Therefore, parameter identification has an important significance for the stability of ship motion control. Aiming at the real-time identification problem of the nonlinear and time-varying ship motion system during movement, this paper reconstructs the ship motion system with the propeller speed and rudder angle as control variables and designs an online identification algorithm with the sliding time window method based on the extended Kalman filter algorithm. In addition, to solve the problem of noise in ship motion data collected in real-time, a real-time wavelet filter is developed to perform online preprocessing of the input data of the identification algorithm. The applicability of the method is further demonstrated via a model-scale Korea Research Institute of Ships and Ocean Engineering container ship free-running experiments in a basin.

Effects of water depth and speed on ship motion control from medium deep to very shallow water

To extend the knowledge on ship manoeuvring and predict ship's behaviour accurately from medium deep to very shallow water, this paper comprehensively investigates water depth and speed effects on ship's manoeuvrability, steering model and motion control through experimental studies. Free running model tests at four water depths and six speeds were conducted using a scaled ship model in the towing tank at Flanders Hydraulics Research (FHR). The influence of under keel clearance and speed on the acceleration tests and zigzag manoeuvres were firstly analysed. Secondly, the shallow water effect on ship steering model was discussed via theoretical analysis and experimental validation. Then, the investigation of the effects of speed and under keel clearance on the ship motion controller's parameters and performance was executed, and a novel adaptive controller was proposed. To improve controller's performance in shallow water, a scheme was proposed to optimize its parameters. The results indicate that both speed and water depth have considerable influence on ship's manoeuvrability and controllability. Especially, water depth restrictions change the hydrodynamic forces, reduce the propulsion efficiency and manoeuvreability, the ship becomes more difficult to manoeuvre and control in shallow water. These impacts on ship manoeuvring, modelling and motion control cannot be neglected.

Towards the Autonomy: Control Systems for the Ship in Confined and Open Waters.

The concept of the Marine Autonomous Surface Ship (MASS) requires new solutions in many areas: from law, through economics, social sciences, environmental issues to the technology and even ethics. It also plays a central role in the work of numerous research teams dealing with the ship motion control systems. This article presents the results of the experiments with application of the selected control methods in automatic steering of the movement of an autonomous ship in the two regimes: during the maneuvers at low speed (in a harbor confined waters) and during the lake trials in open water conditions. In the first case, multidimensional state controller synthesized with Linear Matrix Inequalities (LMI) algorithms was used, while, in the second case, Model Predictive Control (MPC) control was adopted. The object for which the experiments were carried out was 1:24 scale model of the Liquefied Natural Gas (LNG) carrier. The paper presents also the design of the measurement and control system and the user interface. The experiments were conducted in the natural conditions on the lake. The results of the experiments indicate the fundamental role of the measurement system in the process of controlling an autonomous ship.

Dynamic modelling of ship using gaussian processes and sg filter

Dynamic modeling of surface ships is a prerequisite for intelligent navigation and ship motion control. It is easy to ignore the nonlinear and strong coupling of ship dynamics by using traditional mechanism modeling. Moreover, the obtained parameter errors will be large due to the influence of noise and multicollinearity, it is difficult to establish a high-precision ship dynamic model. This study use a data-driven nonparametric bayesian model based on gaussian process regression on dynamic modelling of ship, it can capture the strong nonlinearity and motion coupling in ship motion, and deal with the presence of uncertainty and noise. SG filter is used to smooth the data to reduce the influence of noise on modeling. The results indicate that the gaussian processes regression and the SG filter can reproduce the ship’s motion well, which is beneficial to the further development of ship control design.

DESIGN CONTROL OF SURFACE MARINE VEHICLE USING DISTURBANCE COMPENSATING MODEL PREDICTIVE CONTROL (DC-MPC)

This research studied ship motion control by considering four degrees of freedom (DoF): yaw, roll, sway, and surge in which comprehensive mathematical modeling forming a nonlinear differential equation. Furthermore, this research also investigated solutions for fundamental yet challenging steering problems of ship maneuvering using advanced control method: Disturbance Compensating Model Predictive Control (DC-MPC) method, which based on Model Predictive Control (MPC). The DC-MPC allows optimizing a compensated control then consider sea waves as the environmental disturbances. Those sea waves influence the control and also becomes one of the constraints for the system. The simulation compared the varying condition of Horizon Prediction (Np) and another method showing that the DC-MPC can manage well the given disturbances while maneuvering in certain Horizon Prediction. The results revealed that the ship is stable and follows the desired trajectory

Steering model identification and control design of autonomous ship: a complete experimental study

ABSTRACT Steering ship models are important for the study of autonomous ship manoeuverability and design of ship motion control system. It is always a difficult task to find the mathematical model by first principle as it needs prior knowledge of hydrodynamic derivatives. The input–output-based system identification theory can be used to establish system mathematical models. A solution is offered by developing a Wi-Fi-based self-propelled, autonomous system for a ship model with Internet of Things (IoT) capabilities to perform manoeuvering and seakeeping tests in indoor environment without any complex mechanical structure, viz. following bridge. The developed autonomous on-board system equipped with main computer, suitable electronics, sensors, data acquisition system and Wi-Fi-based communication system. The developed system offers a cost effective, modular and portable solution to perform hydrodynamic studies of different hull form without incorporating major changes in the system. The use of IoT makes the data accessible to a naval architecture in real-time to analyse the motion response of the ship in different wave conditions and enables to implement the digital twin to simulate the real field scenario. Input–output-based model identification experiments such as turning circle and zig-zag tests are conducted to estimate the first-order steering model parameters and is further extended to design and implementation of a classical proportional–derivative-based steering control. The design is described in this paper with details of implementation on a demonstration oceanographic coastal research vessel. It illustrates the excellent communication between shore station computer and the on-board system on a wire-free model with robust control and exhibiting all the motion behaviour and dynamic effects. Experiments performed in wave basin in different wave conditions validate the efficacy of the proffered method.

An efficient ship autopilot design using observer-based model predictive control

An efficient model predictive control design for ship autopilot, which is a representative marine application, is proposed based on projection neural network in this article. Ship motion control at sea exhibits the characteristics of large inertia, strong nonlinearity, and large delay; furthermore, it is frequently influenced by the external disturbances, leading to a complex uncertain problem. In addition, the amplitude of control input—the rudder is constrained. Given the mechanism of on-line computing and the advantages of handling constraints, the model predictive control is one of the most favorable solutions for this problem. Nevertheless, the major challenge of the implementation of traditional model predictive control in application is the computation intensity. In this article, the capability of parallel computation of projection neural network is employed to optimize the objective function formulated by traditional model predictive control method, aiming to improve the computational efficiency. The overall information of ship motion is normally difficult to be obtained; therefore, a state observer should be also included. Extensive studies are conducted to illustrate the effectiveness of the proposed control design.

Optimal universal controllers for roll stabilization

Roll stabilization is an important problem of ship motion control. This problem becomes especially difficult if the same set of actuators (e.g. a single rudder) has to be used for roll stabilization and heading control of the vessel, so that the roll stabilizing system interferes with the ship autopilot. Finding the “trade-off” between the concurrent goals of accurate vessel steering and roll stabilization usually reduces to an optimization problem, which has to be solved in presence of an unknown wave disturbance. Standard approaches to this problem (loop-shaping, LQG, H∞-control etc.) require to know the spectral density of the disturbance, considered to be a “colored noise”. In this paper, we propose a novel approach to optimal roll stabilization, approximating the disturbance by a polyharmonic signal with known frequencies yet uncertain amplitudes and phase shifts. Linear quadratic optimization problems in presence of polyharmonic disturbances can be solved by means of the theory of universal controllers developed by V.A. Yakubovich. An optimal universal controller delivers the optimal solution for any uncertain amplitudes and phases. Using Marine Systems Simulator (MSS) Toolbox that provides a realistic vessel's model, we compare our design method with classical approaches to optimal roll stabilization. Among three controllers providing the same quality of yaw steering, OUC stabilizes the roll motion most efficiently.

Experimental and numerical study of autopilot using Extended Kalman Filter trained neural networks for surface vessels

Due to the nonlinearity and environmental uncertainties, the design of the ship's steering controller is a long-term challenge. The purpose of this study is to design an intelligent autopilot based on Extended Kalman Filter (EKF) trained Radial Basis Function Neural Network (RBFNN) control algorithm. The newly developed free running model scaled surface vessel was employed to execute the motion control experiments. After describing the design of the EKF trained RBFNN autopilot, the performances of the proposed control system were investigated by conducting experiments using the physical model on lake and simulations using the corresponding mathematical model. The results demonstrate that the developed control system is feasible to be used for the ship's motion control in the presences of environmental disturbances. Moreover, in comparison with the Back-Propagation (BP) neural networks and Proportional-Derivative (PD) based control methods, the EKF RBFNN based control method shows better performance regarding course keeping and trajectory tracking.

Prototyping and Simulation Environment of Ship Motion Control System

Prototyping and Simulation Environment of Ship Motion Control System

Automatic Docking for Underactuated Ships Based on Multi-Objective Nonlinear Model Predictive Control

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.

Comparing Combinations of Linear and Nonlinear Feedback Terms for Ship Motion Control

In this article, combinations of linear and nonlinear feedback terms are investigated for 3 degrees-of-freedom pose and velocity control of ships. Nonlinear control algorithms that are found in the literature often have linear feedback terms, which result in nice globally exponential stability properties when assuming no actuator constraints. However, considering that all actuators have saturation constraints, such stability properties are not feasible in practice. Applying nonlinear feedback terms can be a step to handle such constraints. As a result, this article explores nonlinear feedback terms for both the kinematic and kinetic control loops. Specifically, three controllers based on a cascaded backstepping control design are implemented and compared through simulations and model-scale experiments in an ocean basin. Stability properties and tuning rules for all the controllers are also provided. Interestingly, the use of nonlinear feedback terms gives the ability to constrain the feedback control inputs globally while simultaneously being able to change the convergence rates locally. The price to be paid is the introduction of additional tuning parameters. The three controller types are compared using performance metrics which consider both control accuracy and energy use.

State-of-the-Art Research on Motion Control of Maritime Autonomous Surface Ships

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.

Deterministic-Based Ship Anti-Collision Route Optimization with Web-Based Application

Most of the accidents are caused by human error at sea so, decision making process made by navigators should be more computerised and automated. The supported decision making can be a step forward to decrease the risk of collision. This paper, in this respect, aims to present a deterministic approach to support optimum collision avoidance trajectory. This approach involves a collision avoidance course alteration. A web-based application coded with "JavaScript" programming language on the "Processing" software platform which allows the own ship to change her course in a deterministic manner to avoid collision optimally has been introduced. Algorithm structure of the method has been formulated and organized according to the International Regulation for Preventing Collision at Sea (COLREGs). The experimental tests results have revealed that the system is practicable and feasible and considerably outperforms heuristic-based method. It is thought that the developed method can be applied in an intelligent avoidance system on board and provides contribution to ship collision avoidance process, automation of ship motion control and ship traffic engineering.

Simulation comparisons by three strategies of control on Ships’ fin stabilizer control system

Reducing the ship rolling motion is an important subject of the ship motion control. And the fin stabilizer is one of the most effective ship stabilizers, which plays an important role in improving the ship seaworthiness, giving a longer life for the ship, ensuring the equipments on the ship working better and making the ship members feel comfortable, advance battle effectiveness of the naval ship. Since the ship rolling motion has the characters of the nonlinearity and randomicity. The building of the mathematic model is difficult. Therefore, the method, that the exact mathematic model is not needed priorly, will be suitable for the control system. In the strict sense, the PID and MFLAC are in accord with these characters. Simulations are given for the fin stabilizer’s control system with three different control methods: Proportion Integration Differentiation (PID), Model-Free Learning Adaptive Control (MFLAC) and the improved MFLAC based on Multi-Innovation (MI-MFLAC). The principles of the three methods are introduced and the advantages and disadvantages of the three methods are represented. The satisfied control effects are achieved with the latter two control strategies.

An energy-efficient nonlinear robust track keeping control algorithm for the Maritime Silk Road

This paper proposes a nonlinear robust track keeping control algorithm for energy efficiency from the perspective of ship motion control, based on the great significance of the twenty-first Century Maritime Silk Road. Replacing the course error with the driven by the sine function in course keeping loop to serve as the input of the controller, the simulation results tested on the ship autopilot simulation platform show that the controller output (maximum rudder angle) decreases, and the control effect of track keeping is better than the previous one in aspects of energy efficiency and rudder shake inhibition. Otherwise, the proposed nonlinear track keeping control algorithm still works in relatively severe sea states. Substituting the nonlinear feedback for the linear feedback has the generality of control effect improving.