• International Journal of Naval Architecture and Ocean Engineering
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• 제12권1호
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• pp.314-324
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• 2020

Due to the nonlinearity and environmental uncertainties, the design of the ship's steering controller is a long-term challenge. The purpose of this study is to design an intelligent autopilot based on Extended Kalman Filter (EKF) trained Radial Basis Function Neural Network (RBFNN) control algorithm. The newly developed free running model scaled surface vessel was employed to execute the motion control experiments. After describing the design of the EKF trained RBFNN autopilot, the performances of the proposed control system were investigated by conducting experiments using the physical model on lake and simulations using the corresponding mathematical model. The results demonstrate that the developed control system is feasible to be used for the ship's motion control in the presences of environmental disturbances. Moreover, in comparison with the Back-Propagation (BP) neural networks and Proportional-Derivative (PD) based control methods, the EKF RBFNN based control method shows better performance regarding course keeping and trajectory tracking.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.1873-1874
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• 2008

본 연구는 근접상황의 선박 간 충돌회피시스템을 개발하기 위한 연구로서 근거리 조우상황에서 발생하는 선박충돌사고를 감소시키기 위한 선박충돌회피 설계방법을 제시한다. 이 설계모델은 레이다(Radar), 선박자동식별장치(AIS : Automatic Identification System)와 자동조타장치(Autopilot)를 충돌회피시스템에 연계하여 선박 간 충돌 사고를 사전에 예측하고 자동회피 방법을 제안한 것이다.

• 한국항해항만학회지
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• 제30권4호
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• pp.259-265
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• 2006

This paper presents an improved adaptive neural network autopilot based on our previous study for track-keeping control of ships. The proposed optimal neural network controller can automatically adapt its learning rate and number of iterations. Firstly, the track-keeping control system of ships is described For the track-keeping control task, a way-point based guidance system is applied To improve the track-keeping ability, the off-track distance caused by external disturbances is considered in learning process of neural network controller. The simulations of track-keeping performance are presented under the influence of sea current and wind as well as measurement noise. The toolbox for track-keeping simulation on Mercator chart is also introduced.

• 해양환경안전학회지
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• 제25권6호
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• pp.809-818
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• 2019

2020년 1월 1일부터 국제해사기구(IMO)는 전 세계 모든 해역을 지나가는 선박을 대상으로 선박연료유의 황 함유량 상한선을 3.5 %에서 0.5 %로 낮춰 선박으로 인해 발생하는 대기오염을 줄이기 위한 강력한 규제를 실시한다. 황 함유량이 낮은 연료유를 사용하여 대기오염 물질을 줄이는 것도 중요하지만 선박을 경제적으로 운영하여 불필요한 에너지 낭비를 줄이는 것 또한 대기오염 물질을 줄이는데 큰 도움이 된다. 따라서 선박은 잡음의 영향을 받더라도 항로를 정확하게 유지하여야 한다. 항로를 정확하게 추종하기 위해 오토파일럿 시스템이 사용되지만 오토파일럿 시스템의 성능이 아무리 우수하다 하더라도 잡음의 영향을 받게 된다면 성능에 한계를 가진다. 실제 환경에서는 자이로스코프에서 측정잡음이 더해진 회두각이 오토파일럿 시스템의 입력으로 들어가 오토파일럿 시스템의 성능을 저하시킨다. 이와 같은 문제를 해결하기 위해 상태추정에 쓰이는 Kalman Filter를 적용하여 잡음의 영향을 줄여주는 기법이 있지만 이 또한 역시 잡음의 영향을 완전히 제거시키는 것이 불가능하다. 따라서 본 논문에서는 잡음제거 성능을 더욱 더 개선시키기 위해 전진방향 구간에서는 인공지능 기술 중 하나인 다층퍼셉트론(Multi-Layer Perceptron; MLP)를 적용하고, 회전구간에서는 Kalman Filter를 적용하여 Kalman Filter만을 사용한 경우보다 우수한 잡음제거 기법을 제안한다. 시뮬레이션을 통해 제안한 방법이 Kalman Filter만을 사용한 경우보다 조타기의 오동작을 방지하여 선박의 전진방향 운동이 개선됨을 확인할 수 있다.

• International Journal of Aeronautical and Space Sciences
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• 제12권1호
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• pp.78-83
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• 2011

Traditional autopilot design requires an accurate aerodynamic model and relies on a gain schedule to account for system nonlinearities. This paper presents the control architecture applied to a dynamic model inversion at a single flight condition with an on-line neural network (NN) in order to regulate errors caused by approximate inversion. This eliminates the need for an extensive design process and accurate aerodynamic data. The simulation results using a developed full nonlinear 6 degree of freedom model are presented. This paper also presents the stability evaluation for control systems to which NNs were applied. Although feedback can accommodate uncertainty to meet system performance specifications, uncertainty can also affect the stability of the control system. The importance of robustness has long been recognized and stability margins were developed to quantify it. However, the traditional stability margin techniques based on linear control theory can not be applied to control systems upon which a representative non-linear control method, such as NNs, has been applied. This paper presents an alternative stability margin technique for NNs applied to control systems based on the system responses to an inserted gain multiplier or time delay element.

• 대한조선학회논문집
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• 제34권3호
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• pp.19-26
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• 1997

본 논문에서는 신경망을 이용한 선박자동조타장치의 개발에 관한 연구결과를 소개한다. 앞의 논문에서 소개된 Back-Propagation 알고리즘을 이용하여 선박의 자동운항을 위한 자동제어방법을 개발하였으며 그 결과 기준모델추구신경망제어기와 순간최적제어기를 설계하였다. 기준모델추구신경망제어기는 선수각과 선수각속도가 주어진 기준모델을 추구하도록 타각을 제어하도록 하였으며, 순간최적제어기는 현 상태에서 다음상태로의 천이를 최적화하도록 하였다. 신경망에 근거한 이들 제어기법을 간단한 선박조종수치모델에 적용한 결과 그 효용성을 확인할 수 있었다.

• 한국해양공학회지
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• 제27권2호
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• pp.87-92
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• 2013

The variation in the course keeping ability in relation to rudder type is investigated using simulations with 3 different types of rudders (a normal rudder, normal rudder with a plate, and Schilling rudder) under wave conditions. The simulation is developed based on an MMG model with Kijima's regression model, along with the data from Son's experiments and Kose's experiments. A 3-D source distribution method is applied to calculate the source of the external wave forces for the simulation. The coefficients of an autopilot controller that may affect the course keeping ability are also estimated from the simulations with the different rudders. The course keeping ability is evaluated by comparing the forward distances while the ships are simulated with the rudders and autopilot controller.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2018년도 춘계학술대회
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• pp.158-159
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• 2018

전 세계적으로 진행 중인 스마트 선박 개발 관련 국내 외 동향을 분석하고, 스마트 선박 특히 자율운항 선박에 필요한 주요 기능을 식별하여, 자율운항 선박을 위한 INS(Integrated Navigation System) 개발에 활용되어 질 수 있는 핵심 기술을 소개한다.

• 한국항해학회지
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• 제8권1호
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• pp.15-42
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• 1984

Many studies have been done in the field of fuzzy logic theory, but it's application to the ship's steering system is few until this date. This paper is to survey the effect of application of fuzzy logic control by new compositional rule of Inference to the ship's steering system. The controller is made up of a set of Linguistic Control Rules which are conditional linguistic statements connecting the inputs and output, and take the inputs derived from deviation angle and it's angular velocity. The Linguistic Control Rules are implemented on the digital computer to verify the performance of the fuzzy logic controller and simulations have been done in six cases of initial condition and disturbance type. Consequently, it was proved that the ship's steering system by introducing the F.L.C. is performed efficiently and less energy loss system compared with the conventional autopilot.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 춘계학술대회 및 창립 30주년 심포지엄(논문집)
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• pp.67-74
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• 2006

In this paper, an adaptive neural network controller and its application to automatic berthing control of ship is presented. The neural network controller is trained online using adaptive interaction technique without any teaching data and off-line training phase. Firstly, the neural networks used to control rudder and propeller during automatic berthing process are presented. Finally, computer simulations of automatic ship berthing are carried out to verify the proposed controller with and without the influence of wind disturbance and measurement noise.