Ship Maneuvering Motion Research Articles

Identification modeling of ship manoeuvring motion based on ISSA-SVR

This paper introduces the sparrow search algorithm–support vector regression (SSA-SVR) for the first time to improve the precision of mathematical modelling for ship motion through parameter identification. To address the nonsmoothness in ship motion, this study proposes a multistrategy enhanced modelling method named the improved SSA-SVR (ISSA-SVR) for predicting ship motion. Using the KRISO container ship (KCS) test data, this study establishes first-order linear response, second-order nonlinear response, and manoeuvring model group mathematical models through ISSA-SVR identification. The generalisability of the models derived from support vector regression, backpropagation neural networks, particle swarm optimisation-SVR, SSA-SVR, and ISSA-SVR is validated using distinct data from different samples. The results show that the algorithmic model proposed herein excels in prediction compared with other algorithms, thus highlighting its superior generalisation. Furthermore, the proposed method offers solutions with increased accuracy and improved stability for modelling ship motion.

Research on LSTM-Based Maneuvering Motion Prediction for USVs

Maneuvering motion prediction is central to the control and operation of ships, and the application of machine learning algorithms in this field is increasingly prevalent. However, challenges such as extensive training time, complex parameter tuning processes, and heavy reliance on mathematical models pose substantial obstacles to their application. To address these challenges, this paper proposes an LSTM-based modeling algorithm. First, a maneuvering motion model based on a real USV model was constructed, and typical operating conditions were simulated to obtain data. The Ornstein–Uhlenbeck process and the Hidden Markov Model were applied to the simulation data to generate noise and random data loss, respectively, thereby constructing a sample set that reflects real experiment characteristics. The sample data were then pre-processed for training, employing the MaxAbsScaler strategy for data normalization, Kalman filtering and RRF for data smoothing and noise reduction, and Lagrange interpolation for data resampling to enhance the robustness of the training data. Subsequently, based on the USV maneuvering motion model, an LSTM-based black-box motion prediction model was established. An in-depth comparative analysis and discussion of the model’s network structure and parameters were conducted, followed by the training of the ship maneuvering motion model using the optimized LSTM model. Generalization tests were then performed on a generalization set under Zigzag and turning conditions to validate the accuracy and generalization performance of the prediction model.

Dynamic nonparametric modeling of sail-assisted ship maneuvering motion based on GWO-KELM

Accurately predicting the state of ship motion is crucial for ship navigation control with energy efficiency optimization studies. When modeling the motion of novel ships equipped with propulsion devices such as sails, it is essential to consider the effects of sail thrust and lateral forces on the ship's motion states. Building upon the analysis of sail force and sail-assisted ship motion modeling, this paper proposes a ship motion dynamic modeling method utilizing Kernel Extreme Learning Machine (KELM) regression. By optimizing the hyperparameters of the kernel function using the Grey Wolf Optimizer (GWO) algorithm and incorporating a sliding time window for dynamic model updating, the proposed approach combines the efficient learning mechanism of KELM with the global search capability of GWO, effectively enhancing prediction accuracy and timeliness. Utilizing KVLCC2 ship model motion data, simulation data from the sail-assisted ship “New Aden” and real ship data as case studies, comparative analysis with traditional models such as SVM and ELM. The results demonstrate that the GWO-KELM modeling approach exhibits robustness and high prediction accuracy, the MAE of the proposed model in the real ship motion prediction effect is reduced by 8.89% and the RMSE is reduced by 9.44% compared with the other models, which significantly enhance the predictive performance of sail-assisted ship maneuvering motion states.

A temporal prediction model for ship maneuvering motion based on multi-head attention mechanism

In real-world maritime conditions, accurately predicting ship maneuvering motion over ultrashort periods can enhance the development of more precise vessel control algorithms. This study proposes a prediction model for ship maneuvering that utilizes a multi-head attention mechanism. To form a basis for this data-driven approach, we gathered training data from turning and zigzag tests of a ship conducted in natural sea environments. The model's predictive capability is assessed by comparing two strategies over a fixed 30-time-step forecast horizon: iterative multi-step forecasting (IMS) and direct multi-step forecasting (DMS). Notably, the model employs distinct attention layers to examine the temporal relationships among various motion variables across the three degrees of freedom in the output. Moreover, it uses separate attention layers to explore the distinct time response traits of the input motion states and control signals. The results demonstrate that different motion state variables display unique temporal correlations under different signal sequences, and the proposed network architecture effectively captures these temporal response traits.

Four-DOF Maneuvering Motion of a Container Ship in Shallow Water Based on CFD Approach

With the continuous increase in ship size combined with the generally slower increase in the sizes of waterways, the need for the prediction of ship maneuvering in shallow waterways continues to attract attention from the international scientific community. Ship behavior in shallow water is relevant in seabed effects that result in changing the hydrodynamic forces acting on a ship. In this study, the maneuvering characteristics of a container ship with four degrees of freedom in shallow water are analyzed. The Reynolds-Averaged Navier Stokes approach in Ansys Fluent code is used to produce the maneuvering coefficients through the simulations of forward running, static drift, static heel, circular motion, the combined motions, and the pure roll motion of the KRISO container ship. The maneuvering characteristics of the ship are estimated for evaluating the ship behaviors in shallow-water conditions. The obtained results show that the roll has a significant decrease and the ship’s turning diameter has a significant increase when the ship operates in a shallow waterway. The predictions of the maneuvering characteristics of the ship are in good agreement with those of free-running model tests, indicating that the numerical simulation based on the Computational Fluid Dynamics method has compromising capability to predict the maneuvering derivatives and the four-DOF ship maneuvering motion in shallow water as well.

Online prediction of ship maneuvering motions based on adaptive weighted ensemble learning under dynamic changes

Dynamic changes in ship maneuverability challenge the accuracy and effectiveness of ship maneuvering models. This paper proposes an online prediction method based on the adaptive weighted ensemble learning framework, which can adaptively update the model according to changes in maneuverability, especially for reoccurring changes. The method contains two main mechanisms: the change monitoring mechanism and the adaptive weighting mechanism. The former identifies the change in ship dynamics and decides when to incorporate a new base model; the latter adjusts the weights of the base models to align with current scenarios, thus ensuring the predictive accuracy. To assess the method’s effectiveness under varying ship dynamics, the online prediction of ship maneuvering motions under speed-induced dynamic changes is investigated. Compared with the offline model, the result demonstrates the superiority of the adaptive weighted ensemble model. The proposed method can consistently provide accurate predictions in the scenarios with reoccurring changes, and can also enhance the model capability by adjusting weights to cope with some unencountered changes.

Multi-objective optimal input design for grey-box identification modelling of ship manoeuvring motion

ABSTRACT Grey-box modelling has been widely used to predict the ship manoeuvring motion. In order to design input signals for the grey-box model of ship manoeuvring motion, a multi-objective optimal input design (MOID) method is proposed in this paper. In the proposed method, the MOID is regarded as a multi-objective optimization problem (MOP), where both of D-optimality criterion and condition number are taken as two-objective functions to improve the accuracy and robustness of identifying the grey-box model. The correlation factor is also presented as a constraint for reducing the parameter drift. The MOP is solved by the non-dominated sorting genetic algorithm-II (NSGA-II) with a constraint handling technique. The technique for order preference by similarity to an ideal solution (TOPSIS) is also adopted to screen out an optimal solution from the Pareto solutions. The effectiveness of MOID signals is validated by Monte Carlo analysis and grey-box identification of the MMG model.

Adaptive Ensemble of Multi-Kernel Gaussian Process Regressions Based on Heuristic Model Screening for Nonparametric Modeling of Ship Maneuvering Motion

Abstract Gaussian process regression (GPR) is a commonly used approach for establishing the nonparametric models of ship maneuvering motion, and its performance depends on the selection of the kernel function. However, no single kernel function can be universally applied to all nonparametric models of ship maneuvering motion, which may compromise the robustness of GPR. To address this issue, an adaptive ensemble of multi-kernel GPRs based on heuristic model screening (AEGPR-HMS) is proposed in this paper. In the proposed method, four kernel functions are involved in constructing the ensemble model. The HMS method is introduced to determine the weights of individual-based GPR models, which can be adaptively assigned according to the baseline GPR model. To determine the hyper-parameters of these kernel functions, the genetic algorithm is also employed to compute the optimal values. The KVLCC2 tanker provided by the SIMMAN 2008 workshop is used to validate the performance of the proposed method. The results demonstrate that the AEGPR-HMS is an efficient and robust method for nonparametric modeling of ship maneuvering motion.

Black-box modeling of ship maneuvering motion using multi-output least-squares support vector regression based on optimal mixed kernel function

Black-box modeling has been widely used to predict ship maneuvering motion. A novel black-box modeling approach using optimal mixed kernel multi-output least-squares support vector regression (MMLSSVR) based on the artificial hummingbird algorithm (AHA) is proposed in this paper. To improve the performance of the MMLSSVR model, a mixed kernel function that combines a radial basis function and a polynomial kernel function to capture both global and local properties is proposed. The optimal hyper-parameters of the mixed kernel function are determined by the AHA, which employs the linear combination of the mean square error and the determinant of error covariance as the objective function to balance between prediction accuracy and stability. The performance of the proposed method is evaluated using SR108 large container ship data. The experimental results demonstrate that the proposed MMLSSVR model achieves desirable prediction accuracy and strong generalizability for ship maneuvering motion modeling.

An attention mechanism model based on positional encoding for the prediction of ship maneuvering motion in real sea state

An attention mechanism model based on positional encoding for the prediction of ship maneuvering motion in real sea state

Ship Rescue Optimization: A New Metaheuristic Algorithm for Solving Engineering Problems

<p>In recent years, more and more problems in the industry have started to be solved using metaheuristics. In this paper, inspired by the ship maneuvering motion function and the rescue process, we propose ship rescue optimization (SRO) to solve the challenging optimization problem. The ship rescue process can be divided into two types of delayed rescue (large area rescue) and immediate rescue (small rescue) according to the searched person, and we can correspond these two types of rescue behaviors to the search space exploration and exploitation processes, respectively. In this process, SRO simulates the motion process of ship rescue according to the ship maneuvering equation of motion and finally comes up with an optimal position update algorithm. We verified the performance of the proposed algorithm on different dimensions of 57 test functions consisting of CEC2013 and CEC2017 and on three real engineering problems, and compared it with eight current mainstream algorithms. The algorithm proposed in this paper is shown to be robustly applicable in solving challenging optimization problems.</p> <p> </p>

Black-box Modeling of Ship Maneuvering Motion Based on Gaussian Progress Regression Optimized by Particle Swarm Optimization

Black-box Modeling of Ship Maneuvering Motion Based on Gaussian Progress Regression Optimized by Particle Swarm Optimization

Numerical study on static drift tests for ship maneuvering in regular waves

This study is driven by the coupled seakeeping–maneuvering analysis, which predicts ship maneuverability in waves by combining seakeeping analysis with a mathematical maneuvering model. The traditional maneuvering model and its corresponding coefficients are developed based on calm sea conditions, where wave effects are typically disregarded as a temporary compromise. It is an inevitable need to revisit the mathematical maneuvering model or coefficients by considering wave effects. To this end, in this study, a series of static drift tests are employed in waves to investigate the influence of wave effects on the sway damping coefficients of ship maneuvering motion. These tests are conducted using a numerical tank, which overcomes spatial constraints and maintains a reliable wave field for ship–wave interactions. Three wave parameters, namely wavelength, wave steepness, and wave direction, are considered to assess their impacts on the wave-induced motions and forces. Furthermore, the wave effects on the maneuvering coefficients are summarized and discussed using direct observations of ship-turning motions in waves. Additionally, the benefits of computational fluid dynamics analysis are utilized to showcase the strong nonlinear phenomena of ship–wave interactions.

SeaBil: Self-attention-weighted ultrashort-term deep learning prediction of ship maneuvering motion

Accurate prediction of motion dynamics fundamentally promotes the autonomy of intelligent ships, but faces great challenges in modeling mechanism. In this paper, to establish data-driven recurrent mapping within ship motion dynamics, an ultrashort-term deep learning predictor is innovatively developed by elaborately creating a self-attention-weighted bidirectional long short-term memory (Bi-LSTM) network in conjunction with 1-dimensional convolution (Conv-1D), named SeaBil. To be specific, combined with the sliding-window technique, the Conv-1D is devised to convert 5-dimensional-input samples consisting of course angle, yaw rate, roll angle, total speed and rudder angle into 1-D-feature vectors, thereby extracting coupled feature maps within the current data-window. The Bi-LSTM is further deployed to recurrently learn forward and reverse feature maps of ship motion time-series data. Self-attention mechanism cascaded in the serial is employed to adaptively assign time-varying weights of sample instants within the current window such that the ultrashort-term prediction of course angle, yaw rate, roll angle and total speed can be achieved. Eventually, comprehensive comparisons to typical approaches using real-world samples demonstrate the superiority of SeaBil in terms of ultrashort-term prediction accuracy.

Stochastic stabilization and destabilization of ship maneuvering motion by multiplicative noise

Stochastic stabilization and destabilization of ship maneuvering motion by multiplicative noise

Sparse Bayesian Relevance Vector Machine Identification Modeling and Its Application to Ship Maneuvering Motion Prediction

In order to establish a sparse and accurate ship motion prediction model, a novel Bayesian probability prediction model based on relevance vector machine (RVM) was proposed for nonparametric modeling. The sparsity, effectiveness, and generalization of RVM were verified from two aspects: (1) the processed Sinc function dataset, and (2) the tank test dataset of the KRISO container ship (KCS) model. The KCS was taken as the main research plant, and the motion prediction models of KCS were obtained. The ε-support vector regression and υ-support vector regression were taken as the compared algorithms. The sparsity, effectiveness, and generalization of the three algorithms were analyzed. According to the trained prediction models of the three algorithms, the number of relevance vectors was compared with the number of support vectors. From the prediction results of the Sinc function and tank test datasets, the highest percentage of relevance vectors in the trained sample was below 17%. The final prediction results indicated that the proposed nonparametric models had good prediction performance. They could ensure good sparsity while ensuring high prediction accuracy. Compared with the SVR, the prediction accuracy can be improved by more than 14.04%, and the time consumption was also relatively lower. A training model with good sparsity can reduce prediction time. This is essential for the online prediction of ship motion.

Time series prediction of ship maneuvering motion based on dynamic mode decomposition

In order to reveal the dynamic characteristics and achieve rapid time series prediction of ship maneuvering motion, a reduced-order dynamic mode decomposition (DMD) algorithm is applied to reconstruct and predict the zig-zag and turning circle maneuvering motions. A case study is conducted for a KVLCC2 tanker using its free-running model test data. First, all DMD modes are extracted from the test data, and the dominant DMD modes are selected according to their contributions to the dynamical systems of ship maneuvering motions. Then, the dynamical systems of ship maneuvering motions are reconstructed and predicted by the reduced-order and full-order DMD algorithms. A comparison between reduced-order, full-order DMD algorithms and Gaussian process regression (GPR) is conducted. The dynamic characteristics of the dynamical systems are revealed according to the growth rates and frequencies of the dominant DMD modes. The effects of the truncation rank and input samples are analyzed by a parametric study, which indicates that the truncation rank and input samples are crucial to the prediction accuracy. Besides, the computational time of the different algorithms is compared and analyzed.

Improved PER-DDPG based nonparametric modeling of ship dynamics with uncertainty

This study contributes to addressing the challenge of quickly obtaining an effective and accurate nonparametric model for describing ship maneuvering motion in three degrees of freedom (3-DOF). To achieve this, an intelligent ship dynamics nonparametric modeling method named improved PER-DDPG is proposed. This method leverages the deep deterministic policy gradient algorithm (DDPG) and prioritized experience replay mechanism (PER) and analyzes the characteristics between the goal of deep reinforcement learning (DRL) and the modeling process of the nonparametric model. The PER mechanism is utilized to enhance the agent’s understanding of the overall mechanism of ship motion by improving the utilization of samples. The meaning of target value is redefined due to transforming DRL aiming at maximizing cumulative rewards into maximizing the set of immediate rewards at each time step. To validate the performance of the proposed modeling method, we conduct simulation studies using a benchmark ship model i.e., a Mariner cargo ship dynamic model, and experimental studies using a real unmanned surface vehicle (USV). In the simulation test, we demonstrate the effectiveness and generalization of the proposed method through zigzag and turning circle tests. Furthermore, we verify the robustness and applicability of the proposed method by using datasets with uncertain environmental disturbances and datasets with different sampling frequencies. Additionally, the experimental tests conducted on the USV indicate the consistency of the proposed approach.

Nonparametric modeling of ship maneuvering motions in calm water and regular waves based on R-LSTM hybrid method

It is essential to establish reliable method to predict the ship maneuvering motions in complex navigation environments for navigation efficiency and safety. In this paper, a novel hybrid nonparametric modeling method (R-LSTM) is developed by coupling Bidirectional Long Short-Term Memory (Bi-LSTM) with residual network and attention mechanism. Firstly, the prediction capability of the proposed hybrid method is evaluated in the predictions of zigzag maneuvers in calm water. The model test data of a KVLCC2 tanker model are utilized for modeling and validation, and the optimal parameters are determined by Tree-structured Parzen Estimator algorithm. The results demonstrate that the proposed method has higher prediction accuracy and better generalization ability than the classical LSTM method. On this basis, the online modeling method for predicting the turning motion in regular waves is developed along with sliding time window and time series split cross-validation. The model test data of a KCS container ship and a ONRT tumblehome ship under different wave conditions are used for online modeling and real-time prediction. The results indicate that the ship trajectories with trajectory drift and fluctuation features during the turning motion under the wave impacts are captured well by the developed method with satisfactory confidence intervals.

Math-data integrated prediction model for ship maneuvering motion

Highly accurate motion prediction is critical to facilitate safe navigation and autonomous control of a ship. In this paper, a math-data integrated prediction (MDIP) model of ship maneuvering motion is newly developed by combining with mathematical and data-driven modules. The math part is identified by employing variable-order hydrodynamic derivatives which are derived from Taylor expansion. Using extended Kalman filter, an optimal-order mathematical prediction model is obtained. The data-driven part dwells on prediction residuals which are approximated by a least square-support vector machine. Furthermore, integration mechanisms are devised to cohere mathematical and data-driven models as a whole, by deploying summation and neural network approximation, respectively. By comparisons, it is verified that not only order selection but also math-data integration can enhance ship motion prediction accuracy, for which various maneuvering tests are analyzed. The results demonstrate that the proposed MDIP model using math-data integration offers much stronger generalization, thereby paving a new path for ship maneuvering motion prediction.