• International Journal of Naval Architecture and Ocean Engineering
• /
• v.12 no.1
• /
• pp.314-324
• /
• 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.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.1873-1874
• /
• 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.

• Journal of Navigation and Port Research
• /
• v.30 no.4
• /
• pp.259-265
• /
• 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.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.25 no.6
• /
• pp.809-818
• /
• 2019

Since January 1, 2020, the International Maritime Organization (IMO) has put in place strong regulations to reduce air pollution caused by ships by lowing the upper limit of ship fuel oil sulfur content from 3.5% to 0.5% for ships passing through all sea areas around the world. Although it is important to reduce air pollutants by using fuel oil with low sulfur content, reducing the amount of energy waste through the economic operation of a ship can also help reduce air pollutants. Ships can follow designated routes accurately even under the influence of noise using autopilot systems. However, regardless of their quality, the performance of these systems is af ected by noise; heading angles with added measurement noise from the gyroscope are input into the autopilot system and degrade its performance. A technique to solve these problems reduces noise effects through the application of a Kalman filter, which is widely used in condition estimation. This method, however, cannot completely eliminate the effects of noise. Therefore, to further improve noise removal performances, in this study we propose a better denoising method than the Kalman filter technique by applying a multi-layer perceptron (MLP) in forward direction motion and a Kalman Filter in rotation motion. Simulations show that the proposed method improves forward direction motion by preventing the malfunction of a rudder more so than merely using a Kalman Filter.

• International Journal of Aeronautical and Space Sciences
• /
• v.12 no.1
• /
• pp.78-83
• /
• 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.

• Journal of the Society of Naval Architects of Korea
• /
• v.34 no.3
• /
• pp.19-26
• /
• 1997

This paper is concerned with the design of neural-network based autopilot control system. The back-propagation neural network introduced in the previous paper by authors is applied to the autopilot control system. As a result, two neural-network controllers are developed, which are the model reference adaptive neural controller and the instantaneous optimal neural controller. The model reference adaptive neural controller is the control technique that the heading angle and angular velocity are controlled by the rudder angle to follow the output of the reference model. The instantaneous optimal neural controller optimizes the transition from one state to the next state. These control techniques are applied to a simple ship maneuvering model and their effectiveness is proved by numerical examples.

• Journal of Ocean Engineering and Technology
• /
• v.27 no.2
• /
• pp.87-92
• /
• 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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2018.05a
• /
• pp.158-159
• /
• 2018

It analyzed the domestic and overseas trends related to smart ship development in the world and identifies key functions required for smart vessels, especially autonomous vessels, and introduces key technologies that can be utilized in the development of INS(Integrated Navigation System) for autonomous vessels.

• Journal of the Korean Institute of Navigation
• /
• v.8 no.1
• /
• pp.15-42
• /
• 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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2006.06b
• /
• pp.67-74
• /
• 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.