Automatic ship berthing using artificial neural network trained by consistent teaching data using nonlinear programming method

Ahmed, Yaseen Adnan; Hasegawa, Kazuhiko

doi:10.1016/j.engappai.2013.08.009

Cited by 70 publications

(64 citation statements)

References 10 publications

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“…NN algorithm and PID controller are combined to design the new controller [60,88], which can resist high wind and wave disturbance [92]. This good effect is verified by simulation and experiment in the subsequent series of studies [89][90][91]. At present, the NN algorithm extends to wavelet neural network, backpropagation neural network (BPNN) and RBFNN and so on.…”

Section: Number Algorithm Number Algorithmmentioning

confidence: 84%

“…In addition, the combination of predictive algorithm and fuzzy control [80], PID algorithm [85], or the introduction of disturbance observer all adopt different methods to ensure ship stability [86]. The research on automatic docking and undocking control of ships is mainly based on the combination of neural network and PID [87][88][89][90][91][92].…”

Section: Stabilization Controlmentioning

confidence: 99%

“…At present, the optimization algorithm is applied to the optimization of PID control parameters. For example, ACA [66], GA [50,79], PSO algorithm [70] and NN algorithm [87][88][89][90][91][92]119] are applied to the controller to modify the PID parameters, which adapts to different operating conditions of ships and solves the problems of difficult adjustment of PID parameters and poor self-adaptation.…”

Section: Proportion Integral Derivative Algorithmmentioning

confidence: 99%

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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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Section: Number Algorithm Number Algorithmmentioning

confidence: 84%

Section: Stabilization Controlmentioning

confidence: 99%

Section: Proportion Integral Derivative Algorithmmentioning

confidence: 99%

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

Wang

Liu

et al. 2019

JMSE

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“…Trajectory design is needed in automatic berthing control. Ahmed et al enabled a ship to navigate along an imaged route and stop at a given place 1.5 times the ship's length from the berth, which means they can complete the automatic berthing control task [6,7]. A value of 1.5 times the ship's length is set to allow sufficient maneuvering space to cope with any adverse situation, so berthing maneuvering does not necessarily bring the ship close to the berth but can also stop the ship at a certain distance outside the berth.…”

Section: Introductionmentioning

confidence: 99%

Research into the Automatic Berthing of Underactuated Unmanned Ships under Wind Loads Based on Experiment and Numerical Analysis

Piao

Guo

Sun

2019

JMSE

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With the continuous improvement of unmanned ship automation requirements, research into the automatic berthing of underactuated unmanned ships has important theoretical significance and practical value. In order to determine the trajectory of unmanned ships, the line of sight (LOS) algorithm was applied due to the characteristics of underactuated unmanned ships without side thrusters. In order to resist wind disturbance, the active disturbance rejection control (ADRC) method was applied to keep the ship moving on its intended trajectory. Then, to carry out the simulation analysis before the tank experiment, a remote-control simulation system based on a user datagram protocol (UDP) communication was built, and the ability of the ADRC controller to make the ship perform completely automatic berthing in both wind and no wind conditions was verified in simulations. Combined with the simulation results, a tank experiment was accomplished at the Japanese National Research Institute of Fishery Engineering. The experimental results also showed that the ADRC controller has good robustness, that the problems of insufficient autonomous route determination and the disturbance rejection ability in the process of the automatic berthing of underactuated unmanned ships are solved, and the safety of ship navigation is improved, which lays a theoretical and experimental foundation for the further development of unmanned ship control.

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“…Combined with AI computation, the controller performance can be greatly improved [22]. In the field of ship motion control, during last decades there have been many NN applications e.g., [8,9,23,24]. Comparatively, SVM is a novel technique of Artificial Intelligence.…”

Section: Introductionmentioning

confidence: 99%

Control for Ship Course-Keeping Using Optimized Support Vector Machines

Luo

Cong

2016

Algorithms

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Support vector machines (SVM) are proposed in order to obtain a robust controller for ship course-keeping. A cascaded system is constructed by combining the dynamics of the rudder actuator with the dynamics of ship motion. Modeling errors and disturbances are taken into account in the plant. A controller with a simple structure is produced by applying an SVM and L2-gain design. The SVM is used to identify the complicated nonlinear functions and the modeling errors in the plant. The Lagrangian factors in the SVM are obtained using on-line tuning algorithms. L2-gain design is applied to suppress the disturbances. To obtain the optimal parameters in the SVM, then particle swarm optimization (PSO) method is incorporated. The stability and robustness of the close-loop system are confirmed by Lyapunov stability analysis. Numerical simulation is performed to demonstrate the validity of the proposed hybrid controller and its superior performance over a conventional PD controller.

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Automatic ship berthing using artificial neural network trained by consistent teaching data using nonlinear programming method

Cited by 70 publications

References 10 publications

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

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

Research into the Automatic Berthing of Underactuated Unmanned Ships under Wind Loads Based on Experiment and Numerical Analysis

Control for Ship Course-Keeping Using Optimized Support Vector Machines

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