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

Zhu, Man; Hahn, Axel; Wen, Yuanqiao; Bolles, A. Lynn

doi:10.1016/j.apor.2017.09.006

Cited by 68 publications

(25 citation statements)

References 19 publications

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“…Several models used for ship maneuvering were mentioned in [19]. Nonlinear static models, such as the response model of sway motion, the Mathematical Maneuvering Group (MMG) response model, and the Abkowitz model, can be employed as the RM.…”

Section: Selection Of Rmmentioning

confidence: 99%

“…However, the maneuver scheme for identification was not suitable as good seamanship handling required [18]. Zhu used a least squared SVM and optimized it with the artificial bee colony method, but the surge velocity was not verified for a zigzag test [19]. Luo proposed a method in which the reconstruction of sample data was identified using an SVM, and no verification was conducted for the turning test [20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ship Maneuvering Prediction Using Grey Box Framework via Adaptive RM-SVM with Minor Rudder

Mei

Sun

Shi

et al. 2019

Polish Maritime Research

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A grey box framework is applied to model ship maneuvering by using a reference model (RM) and a support vector machine (SVM) (RM-SVM). First, the nonlinear characteristics of the target ship are determined using the RM and the similarity rule. Then, the linear SVM adaptively fits the errors between acceleration variables of RM and target ship. Finally, the accelerations of the target ship are predicted using RM and linear SVM. The parameters of the RM are known and conveniently acquired, thus avoiding the modeling process. The SVM has the advantages of fast training, quick simulation, and no overfitting. Testing and validation are conducted using the ship model test data. The test case reveals the practicability of the RF-SVM based modeling method, while the validation cases confirm the generalization ability of the grey box framework.

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Section: Selection Of Rmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ship Maneuvering Prediction Using Grey Box Framework via Adaptive RM-SVM with Minor Rudder

Mei

Sun

Shi

et al. 2019

Polish Maritime Research

View full text Add to dashboard Cite

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“…The large MASS described in this paper refers to a ship with a length of more than 150 m according to the IMO ship construction [28], which is currently in the stage of step-by-step research. The research on large conventional ships focuses on the structure and maneuverability of its hull model [29][30][31][32], propulsion and power system control [33][34][35][36], risk analysis [37,38], etc. However, the research on motion control in course control, docking and trajectory tracking are mostly concentrated in the stage of simulation and verification (Section 4).…”

Section: Characteristics Of Large Massmentioning

confidence: 99%

“…GA, PSO, SVM, ACO, FFOA, ABC, and FA algorithms are often used for ship model parameters identification [32,[195][196][197][198][199][200][201] or ship controller parameter adjustment. For example, optimizing ship fuzzy controller by introducing GA and PSO algorithm [108,202,203]; Optimizing the tuning parameters of ship PID controller by introducing GA and ACO [50,66]; Optimizing the course controller parameters based on SVM by introducing PSO [199]; Or optimizing the parameters of controller based on backstepping control and disturbance observer by FA [69].…”

Section: Number Algorithm Number Algorithmmentioning

confidence: 99%

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

Wang

Liu

et al. 2019

JMSE

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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.

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“…However, the shortages of the parallel-in-time approach are the unnecessarily strong nonlinearity of the optimization problem and the poor convergence behaviors to the desired reference trajectory [27,28]. Optimization theories have been widely used in marine research [29][30][31]. To effectively solve the programming problem using embedded platforms, automatic code generation is a widely discussed issue.…”

Section: Introductionmentioning

confidence: 99%

A Crane Overload Protection Controller for Blade Lifting Operation Based on Model Predictive Control

2018

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Lifting is a frequently used offshore operation. In this paper, a nonlinear model predictive control (NMPC) scheme is proposed to overcome the sudden peak tension and snap loads in the lifting wires caused by lifting speed changes in a wind turbine blade lifting operation. The objectives are to improve installation efficiency and ensure operational safety. A simplified three-dimensional crane-wire-blade model is adopted to design the optimal control algorithm. A crane winch servo motor is controlled by the NMPC controller. The direct multiple shooting approach is applied to solve the nonlinear programming problem. High-fidelity simulations of the lifting operations are implemented based on a turbulent wind field with the MarIn and CaSADi toolkit in MATLAB. By well-tuned weighting matrices, the NMPC controller is capable of preventing snap loads and axial peak tension, while ensuring efficient lifting operation. The performance is verified through a sensitivity study, compared with a typical PD controller.

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

Cited by 68 publications

References 19 publications

Ship Maneuvering Prediction Using Grey Box Framework via Adaptive RM-SVM with Minor Rudder

Ship Maneuvering Prediction Using Grey Box Framework via Adaptive RM-SVM with Minor Rudder

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

A Crane Overload Protection Controller for Blade Lifting Operation Based on Model Predictive Control

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