A ship's minimum-time maneuvering system using neural networks

Mizuno, Naoki; Tamai, Yutaka; Okazaki, Tadatsugi; Ohtsu, Kohei

doi:10.1109/iecon.2002.1185252

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…The results showed that in real sea condition control performance were greatly affected by Mizuno et al (2002) proposed a minimum time maneuvering method with two types of NNs. The experimental results showed that this method has some advantages in the tracking performance under rough sea condition compared with the previous studies.…”

Section: Introductionmentioning

confidence: 94%

“…The experimental results showed that this method has some advantages in the tracking performance under rough sea condition compared with the previous studies. In addition, some studies in which the NNs are used in order to compensate the nonlinear dynamics of the ship during the tracking or berthing phase have bee conducted (Namkyun & Hasegawa, 2001, 2002Yao et al, 1996Yao et al, , 1997. However, the online learning speeds of the NNs proposed in these studies were slow to obtain the good transient performance for tracking or berthing in real situations.…”

Section: Introductionmentioning

confidence: 99%

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Minimum time ship maneuvering method using neural network and nonlinear model predictive compensator

Mizuno

Kuroda

Okazaki

et al. 2007

Control Engineering Practice

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In this paper, a new minimum time ship maneuvering method using neural network (NN) and nonlinear model predictive compensator is proposed. In this proposed method the NN is used for interpolating the precomputed minimum time solution for real time situations and the nonlinear dynamical model of a ship is used for compensating the control error caused by some modeling errors, disturbances and so on. The introduction of the nonlinear model into the online control system is inspired by the idea that since the nonlinear dynamical model of a ship has been constructed for the off-line numerical computation of the optimal solutions, it could also be used to enhance online control performance. In order to investigate this method, simulation studies and actual sea tests were carried out using a training ship Shioji Maru.The results showed that the system gives approximate solutions in a short computing time and good tracking performance in the actual sea trials. r

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Minimum time ship maneuvering method using neural network and nonlinear model predictive compensator

Mizuno

Kuroda

Okazaki

et al. 2007

Control Engineering Practice

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show abstract

“…Lee et al [4,5] used fuzzy control and LOS algorithms to experiment with a 4 m-long boat to solve the problem of small boat position and navigation accuracy to achieve side booster-assisted berthing. Mizuno et al [6][7][8] solved the problem of automatic berthing under uncertainty disturbances using an artificial neural network approach.…”

Section: Introductionmentioning

confidence: 99%

Failure Mode Analysis of Intelligent Ship Positioning System Considering Correlations Based on Fixed-Weight FMECA

Luo

Zhang

et al. 2022

Processes

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Currently, intelligent ships are still in the early stages of development in terms of autonomous navigation and autonomous berthing, so almost no source of fault data can be obtained. Conducting an in-depth analysis of the failure modes of intelligent ships is critical to optimizing the design of smart ships and ensuring their normal and safe navigation. In this paper, the fixed-weight Failure Mode Effects and Criticality Analysis (FMECA) is combined with the decision-making trial and evaluation laboratory (DEMATEL) method to analyze the failure modes and effects of intelligent ship positioning systems. This combined method not only overcomes the failure of traditional FMECA methods to differentiate between severity, incidence, and detection rates but also allows the correlation of failure causes to be analyzed, bringing the results of the analysis closer to reality. Through the expert scoring of failure modes, the failure modes of this system are risk-ranked, and the key failure causes of this system are identified. Correlations between the critical failure causes are then considered. According to the analysis results, the high-accuracy attitude sensor was identified as the subsystem with the highest level of risk. Unavoidable, unknown failures and environmental factors were found to be key factors in causing positioning system failures. The conclusions can provide a reference for the design of equipment safety for intelligent ship positioning systems.

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Review on the Research of Ship Automatic Berthing Control

Zhang

Ding

et al. 2021

Offshore Robotics

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A ship's minimum-time maneuvering system using neural networks

Cited by 6 publications

References 6 publications

Minimum time ship maneuvering method using neural network and nonlinear model predictive compensator

Minimum time ship maneuvering method using neural network and nonlinear model predictive compensator

Failure Mode Analysis of Intelligent Ship Positioning System Considering Correlations Based on Fixed-Weight FMECA

Review on the Research of Ship Automatic Berthing Control

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