Ship Dynamic Model Research Articles

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.

“PREParE SHIPS” for Automated Ship Passages by Modern Decision Support Tools by Exchanging Future Positions

Misunderstanding the intentions of other vessels is one of the most common causes of ship collisions today. Such misunderstandings are particularly dangerous during navigation in restricted areas, such as fairways, port areas, and inland water ways, and may be exacerbated with the introduction of unmanned vessels. To ensure safe operations, the understanding of the intentions and future positions of manned and unmanned vessels, the PREParE SHIPS project is creating a system capable of determining a ship’s position with high accuracy, predicting its future positions, and communicating the current and future positions. The positioning sub-system combines the signals of Galileo and EGNOS, EGNSS and SBAS respectively, with land based RTK corrections, providing a positioning accuracy of centimetres. Furthermore, by taking advantage of Galileo’s authentication features, the positioning system provides enhanced security measures against spoofing attacks. The prediction sub-system will use a ship dynamic model and machine learning to predict the future positions of the ship and improve said predictions with time, while the communication sub-system will broadcast the prediction through VDES ship2ship and ship2shore. PREParE SHIPS will improve the navigation and handling of ships, through high accuracy positioning and dynamic predictions, as well as reduce the risk of ship collisions, through the communication of present and predicted future positions. PREParE SHIPS is expected to increase safety and efficiency significantly and will be the base of future autonomous operations and standardisations.

A Hybrid Approach to Motion Prediction for Ship Docking—Integration of a Neural Network Model Into the Ship Dynamic Model

While automatic controllers are frequently used during transit operations and low-speed maneuvering of ships, ship operators typically perform docking maneuvers. This task is more or less challenging depending on factors, such as local environment disturbances, the number of nearby vessels, and the speed of the ship as it docks. This article proposes a tool for onboard support that offers position predictions based on an integration of a supervised machine learning (ML) model of the ship into the ship dynamic model. The ML model is applied as a compensator of the unmodeled behavior or inaccuracies from the dynamic model. The dynamic model increases the amount of predetermined knowledge about how the vessel is likely to move and, thus, reduces the black-box factor typically experienced in purely data-driven predictors. A prediction horizon of 30 s ahead of real time during docking operations is examined. History data from the 29-m coastal displacement ship Research Vessel (RV) Gunnerus are applied to validate the approach. Results show that the inclusion of the data-based ML model significantly improves the prediction accuracy.

System identification of ship dynamic model based on Gaussian process regression with input noise

As a critical step designing the ship controller and the maritime traffic simulator, the system identification of a ship dynamic model from input-output data is a promising direction. However, the noise in the training data significantly reduces the accuracy in identification and prediction. Here, we present a robust nonparametric system identification technique for a ship maneuvering model based on Gaussian Process (GP) regression. To solve the problem caused by noise, the input noisy Gaussian Process (NIGP) model is employed, and it can automatically propagate the input uncertainty to the output in the learning model using the Taylor approximation. Polluted simulation datasets obtained from the numerical model of a parametric container ship are used for training. Zigzag and turning circle maneuvering motions are performed, and a comparison with GP and SVM is implemented to validate the proposed approach. The results indicate that the developed scheme for system identification of ships is accurate and robust with noisy input.

선박 동적 모델을 고려한 하이브리드 RDP 시스템의 운영 제어 알고리즘 개발

선박 동적 모델을 고려한 하이브리드 RDP 시스템의 운영 제어 알고리즘 개발

A simpler approach to analysis ship maneuvering performances of hybrid propulsion ship using a HILS

A simulation verification of the hybrid propulsion ship considering accurate electro-dynamic model is required to evaluate ship maneuverability before the maritime demonstration. This paper presents the simplified model constructions for the hybrid propulsion ship using a real time simulator in terms of the dynamic models accuracy and the improvement of a computation speed. In addition, the hardware in the loop simulator based on a laboratory virtual instrument engineering workbench using the ship dynamic model and the developed equipment is proposed to evaluate the electro dynamic characteristics of hybrid propulsion ship. The validity of a proposed model on a hardware in the loop simulator is verified by simulation compared with a detailed model on an electronic circuit simulation software.

Adaptive modeling of maritime autonomous surface ships with uncertainty using a weighted LS-SVR robust to outliers

Maritime Autonomous Surface Ships (MASS) have become increasingly interesting for the commercial maritime sectors as an alternative to conventional ships. For the purpose of development of MASS operations such as motion control (e.g. collision avoidance, trajectory tracking), the ship dynamic model is of significant importance for technique tests, i.e. verification and validation. The ship dynamic model should be suitable for cases which means the sufficient balance between the complexity and the accuracy of the model. This contribution is aiming to develop a robust ship dynamics modeling approach by making efforts on determining a common ship dynamic model and designing a robust identification method. A response model, i.e. the first-order nonlinear Nomoto model widely-used in ship autopilot design is selected. A robust optimal identification method named optimal DW-LSSVR is proposed by taking advantages of least square support vector regression algorithm (LS-SVR), robust 3α principle (D), adaptive weight technique (W), and artificial bee colony algorithm (ABC for optimization). The robust 3α principle has the function of outlier detection. Adaptive weight technique can adaptively weight support vectors to improve the sparsity of LS-SVR. Furthermore, ABC is served as the structural parameters optimizer for LS-SVR. The ships studied in this work are considered as more realistic objects with uncertain parameters induced by unmodeled terms, variation of the loading condition as well as environmental disturbances, and measurement noises. In identifying the widely-used response model for ships, the optimal DW-LSSVR method is verified and validated on both experimental measurements and simulated data. In simulation tests, the effectiveness of the proposed method in identifying the response model has been demonstrated with the use of simulated data including ones with uncertainties which are generated by activating a response model with predefined parameter values by means of step inputs in Monte Carlo simulations. Besides the simulation tests, the experimental investigation on a real Unmanned Surface Vessel (USV) using experimental zigzag maneuvers also indicates the consistency of the proposed approach.

Collision Avoidance Systems for Maritime Autonomous Surface Ships Considering Uncertainty in Ship Dynamics

Many Collision Avoidance Systems (CAS) for autonomous ships usually presume that a ship’s dynamics are completely known in advance. However, precise parameters for ships in different operating conditions are, in fact, uncertain and unknown. The parameter identification of ship dynamics is challenging and time-consuming. Thus, uncertainties in the ship dynamic model are inevitable, which can lead to errors between real trajectories and predicted trajectories. These errors might result in an unexpected collision between ships. Therefore, it is necessary to consider tracking errors in the CAS, which is missing in most existing CAS. This article proposes a way to incorporate the errors in CAS. Specifically, a Velocity Obstacle (VO) algorithm is employed to find collision-free velocities with estimated tracking errors. Firstly, the ship is assumed to be a “black box” whose inputs and outputs are observable, while the internal workings are unknown. Secondly, parameters optimization of a PID controller are employed to determine the best feedback gains for tracking given trajectories; Thirdly, the maximal tracking errors for controlling the ship to arbitrary velocities are estimated. Finally, the maximal error is added to the safety distance and the VO algorithm is employed to find a collision-free solution. The proposed Unknown-Dynamics CAS (UD-CAS) can support the upgrade of existing conventional ships to Type I-III maritime autonomous surface ship.

A Novel Adaptive Weighted Least Square Support Vector Regression Algorithm-Based Identification of the Ship Dynamic Model

This study contributes to developing a novel hybrid identification method based on intelligent algorithms, i.e. the least support vector regression algorithm (LS-SVR) and the artificial bee colony algorithm (ABC), to deal with the identification of the simplified ship dynamic model while the outliers exist in the measurements. The ship dynamic model is directly derived from our previous work which has been well verified and validated. The outliers are detected by introducing the robust estimation method namely the $3\sigma $ principle and then deleted from the training data. The weighted version of LS-SVR (WLS-SVR) with spareness and robustness ability is used as the fundamental identification approach. To improve the performance of the WLS-SVR, the structural parameters involved in it are optimized by utilizing the artificial bee colony algorithm (ABC), and the weights of it are adaptively set with the use of the adaptive weight method. Two case studies including the simulation study on a container ship and the experimental study on an Unmanned Surface Vessel (USV) are carried out to test the proposed hybrid intelligent identification method. The simulation study demonstrates the effectiveness and the acceptable time complexity in terms of the engineering application of the proposed identification method through the comparison with the cross-validation method and particle swarm optimization algorithm optimized LS-SVR. In the experimental study, ABC-LSSVR, ABC-LSSVR with the $3\sigma $ principle (D-ABC-LSSVR), ABC-LSSVR with the adaptive weight (ABC-AWLSSVR), and ABC-LSSVR with both the $3\sigma $ principle and the adaptive weight (D-ABC-AWLSSVR) are applied to identify the steering model for the USV. The results indicate that the influence of the outliers on model identification is effectively diminished by the robust $3\sigma $ principle and the adaptive weight method and that the D-ABC-AWLSSVR outperforms over the other three identification methods in terms of the mean squared error (MSE) of the model predictions.

Towards Automatic Steering of Underactuated Ships

Simple control solutions for steering underactuated ships have been seldom studied in literature. This paper provides a control method for automatic steering of underactuated ships in presence of disturbances and in uncertain environmental conditions. A mathematical model of ship dynamics is developed and validated. Our control method involves introduction of a preparation phase consisting of three algorithms. Waypoints leading to every desired set-point are defined and a continuous Dubins path is generated between these waypoints. A second-order motion profile is assigned to the path to suggest the speed with the best timing for the voyage. The control objective is reformulated from a tracking problem to a regulating problem by changing coordinates of the reference signal. PD-controllers are employed in closed loop to reduce position and heading error. The controller parameters are set in the design phase and do not need additional tuning for different trajectories and ship parameters. Weather information together with the planned motion profile are used in a feedforward controller design to achieve better disturbance rejection and higher accuracy. Performance and robustness of the design are evaluated in simulation by Course-keeping and Course-changing experiments in extreme and uncertain environmental conditions. While the developed control method is simple compared to the methods in literature, it can still achieve quite satisfactory steering performance.

Dynamically Positioned Ship Steering Making Use of Backstepping Method and Artificial Neural Networks

Abstract The article discusses the issue of designing a dynamic ship positioning system making use of the adaptive vectorial backstepping method and RBF type artificial neural networks. In the article, the backstepping controller is used to determine control laws and neural network weight adaptation laws. The artificial neural network is applied at each time instant to approximate nonlinear functions containing parametric uncertainties. The proposed control system does not require precise knowledge of the model of ship dynamics and external disturbances, it also eliminates the problem of analytical determination of the regression matrix when designing the control law with the aid of the adaptive backstepping procedure.

Optimization of warship formation air defense combat model based on dynamic scheduling of combat resources

Aiming at the problem of limited resources and operational time combat air defense combat, naval combat formation analysis dynamic resources scheduling needs, on the basis of the establishment of ship dynamic scheduling model of warship formation combat resources dynamic combat formation resource scheduling, the operational resources and operational objectives, relationship between reasonable allocation of resources to play the warship formation combat operations the maximum efficiency. The design of three kinds of air defense command models: centralized platform free attack mode, command mode, request attack mode of air defense strategy, put forward the dynamic scheduling hybrid algorithm for solving the model, according to the naval fleet air defense combat scenario example for simulation. The simulation results show that the air defense strategy with the request attack mode can have effects on multi-target attacks under the shared combat situation, and the hybrid algorithm based on combat resource dynamic scheduling can meet the real-time operational requirements.

Mitigating Power Fluctuations in Electric Ship Propulsion With Hybrid Energy Storage System: Design and Analysis

Shipboard electric propulsion systems experience large power and torque fluctuations on their drive shaft due to propeller rotational motion and waves. This paper explores new solutions to address these fluctuations by integrating a hybrid energy storage system (HESS) and exploring energy management (EM) strategies. The HESS combines battery packs with ultracapacitor banks. Two strategies for real-time EM of HESS are considered: one splits the power demand such that high- and low-frequency power fluctuations are compensated by ultracapacitors and batteries, respectively; another considers the HESS as a single entity and designs an EM strategy to coordinate the operations of the ultracapacitors and batteries. For both strategies, model predictive control is used to address power tracking and energy saving under various operating constraints. To quantitatively analyze the performance of HESS and its associated controls, a propeller and ship dynamic model, which captures the underlying physical behavior, is established to support the control development and system optimization. Power fluctuation mitigation and HESS loss minimization, the main objectives, are evaluated in different sea conditions. Simulation results show that the coordination within HESS provides substantial benefits in terms of reducing fluctuations and losses.

Identification-based simplified model of large container ships using support vector machines and artificial bee colony algorithm

The 6 degrees of freedom (DOF) model with a high degree of complexity for capturing ship dynamics is generally able to track the nonlinear and coupling dynamics of ships. However, the 6 DOF model makes challenges in estimating model coefficients and designing the model-based control. Therefore, simplified ship dynamic models within allowed accuracy are essential. This paper simplified the 6 DOF nonlinear dynamic model of ships into two decoupled models including the speed model and the steering model through reasonable assumptions. Those models were tested through maneuvering simulations of a container ship with a 4 DOF dynamic model. Support vector machines (SVM) optimized by the artificial bee colony algorithm (ABC) was used to identify parameters of speed and steering models by analyzing the rudder angle, propeller shaft speed, surge and sway velocities, and yaw rate from simulated data extracted from a series of maneuvers made by the container ship. Comparisons with the first order linear and nonlinear Nomoto models show that the simplified nonlinear steering model can capture more complicated dynamics and performs better. Additionally, comparisons among three different parameter identification methods demonstrate similar identification results but the different performance involving the applicability and effectiveness. SVM optimized by ABC is relatively convenient and effective for parameter identification of ship simplified dynamic models.

Innovative Fast Time Simulation Tools for Briefing / Debriefing in Advanced Ship Handling Simulator Training and Ship Operation

The innovative “Simulation-Augmented Manoeuvring Design, Monitoring & Control” system (SAMMON) based on Fast Time Simulation (FTS) technology was developed at the Institute for Innovative Ship Simulation and Maritime Systems (ISSIMS) of the Maritime Simulation Centre Warnemuende MSCW. The system consists of software modules for (a) Manoeuvring Design & Planning, (b) Monitoring & Control based on Multiple Dynamic Prediction and (c) Trial & Training. It is based on complex ship dynamic models for rudder, thruster or engine manoeuvre simulation under different environmental conditions. It is an effective tool for lecturing and demonstrating ship's motion characteristics, as well as for ship handling simulator training. It allows the trainee to immediately see the results of the actual rudder, engine or thruster commands, without having to wait for the real-time response of the vessel. The Maritime Simulation Centre of AIDA Cruises at Rostock/Germany and the CSMART Center for Simulator Maritime Training of Carnival Corporation at Almere/NL have some experience with the use of this new technology to improve simulator training in Advanced Ship Handling Training courses. Examples of its application in briefing/debriefing and introductory lectures for simulator exercises specifically for typical cruise ships with Twin-Screw and Rudder systems will be presented in the paper and at the conference.

Ship forward speed loss minimization using nonlinear course keeping and roll motion controllers

Ship forward speed can be significantly reduced by the added resistance generated by the effect of wave motion. Controlling the ship motions using the actuators available can reduce this speed loss. The use of ship rudder for both steering and roll reduction is considered here with the aim of reducing speed loss. The added resistance due to the wave effects is calculated by the extended radiated energy method and the added resistance in calm water, caused by steering, is also included. In order to judge the speed loss that occurs, a performance index is proposed to quantify the percentage of speed loss. A control system based on the sway–yaw and the roll dynamics is proposed to reduce the ship speed loss. The controller comprises two sliding mode controllers developed using ship dynamic models. Finally, an example of the control of a navy vessel with two rudders is presented to demonstrate the effectiveness of the proposed solution.

Non-intrusive polynomial chaos for efficient uncertainty analysis in parametric roll simulations

Monte Carlo analyses are generally considered the standard for uncertainty analysis. While accurate, these analyses can be expensive computationally. Recently, polynomial chaos has been proposed as an alternative approach to the estimation of uncertainty distributions (Hosder et al. A non-intrusive polynomial chaos method for uncertainty propagation in CFD simulations. In: 44th AIAA aerospace sciences meeting and exhibit, Reno, Nevada, 2006; Wu et al. Uncertainty analysis for parametric roll using non-intrusive polynomial chaos. In: Proceedings of the 12th international ship stability workshop, Washington, DC, USA, 2011). This approach works by representing the function as a series of orthogonal polynomials; the weights for which can be calculated via several methods. Previous studies have demonstrated the usefulness of this technique for comparatively simple systems such as parametric roll modeled by the Mathieu equation with normally distributed parameter values (Wu et al. Uncertainty analysis for parametric roll using non-intrusive polynomial chaos. In: Proceedings of the 12th international ship stability workshop, Washington, DC, USA, 2011). In the present work, a polynomial chaos method is applied to a nonlinear computational ship dynamics model with normally distributed input parameters. Test cases were selected where parametric roll was expected to potentially occur. The resulting probability distributions are compared to the results of a Monte Carlo analysis. In general, these results demonstrate good agreement between Monte Carlo simulation and polynomial chaos in the absence of capsize with significant computation gains found with polynomial chaos. Overall, we conclude that polynomial chaos is an effective tool for reducing simulation time costs when studying parametric roll, and potentially other ship dynamics phenomena, particularly in the absence of capsize-like bifurcations.

An Overall Ship Propulsion Model for Fuel Efficiency Study

Abstract An overall ship propulsion plant involving marine engine, propeller and ship dynamic model was presented in this work. The cycle mean value model was utilized to describe the operation process in engine dynamic, intake/exhaust and turbocharger system. The ship shafting system was modelled using the power balance and its efficiency. The predicted results of fuel consumption, engine delivered power and vessel speeds were tested with measured data under different engine response. The whole ship voyage model will be used to predict fuel consumption and exhaust emissions under different sailing conditions in further study.

Continuous-time system identification of the steering dynamics of a ship on a river

In this study, we consider the parameter estimation problem of a ship dynamics model. We consider two possible approaches to identify a continuous-time model from real data obtained on a river, where the presence of disturbances is a key issue. The first approach is identification through optimisation using a disturbance observer. The second approach corresponds to the refined instrumental variable method for linear parameter varying systems. In addition, we evaluate the accuracy of the parameter estimation through a sensitivity analysis. The obtained results show an improvement in the parameter estimates compared to identification procedures that do not consider the river disturbances. The application of the model for track-keeping control is also illustrated.

Parallel Simulation Based Predictive Control Scheme With Application To Approaching Control For Automatic Berthing

This paper presents a parallel simulation based predictive control algorithm for automatic berthing. The proposed scheme uses many number of control sequences for on-line simulation of future behaviour of the ship movement. Each control sequence used in simulation generates a predicted sequence of the ship's future course. The predicted courses of the ship are evaluated based on some cost function suitable for control objective and then, the ‘optimal’ control signal is applied to the ship. To simulate the future behaviour of the ship, it is used a non-linear ship's dynamic model (called MMG model) and also the massively parallel computational hardware (called GPU: Graphical Processing Unit). Application of GPU for simulation (GPGPU) permits directly use of the nonlinear model of the ship for parallel simulation and makes the control algorithm with highly computational demand feasible. In order to investigate the effectiveness of the proposed algorithm, computer simulations and actual sea tests are carried out using a training ship Shioji Maru. The experimental results show that the proposed method is successfully applied to the approaching control for automatic berthing.