In the field of study about the maritime safety system various reports have been given on the research of ship auto control over a long time but lack of auto control designed for auto berthing control. This paper deals with ship"s track keeping control on

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. Secondly, computer simulations of automatic ship berthing are carried out in Pusan bay to verify the proposed controller under the influence of wind disturbance and measurement noise. The results of simulation show good performance of the developed berthing control system.

본 연구에서는 조이스틱을 이용하여 프로펠러와 타, 선수/선미 쓰러스터를 갖는 선박의 접이안을 위한 제어 알고리즘을 개발하였다. 조이스틱으로부터 전진 방향 및 회전 방향의 속도명령을 받아 전진 방향 및 회전 방향의 속도를 제어하는 MIMO(Multi-Input Multi-Output) 비선형 제어 알고리즘을 개발하기 위해 저속 조종수학모형을 사용하였다. 또한, 본 연구에서는 비선형 및 PID 제어기의 성능을 검증하기 위해 선박 접이안 가상 HILS(Hardware in the Loop Simulation) 프로그램을 구현하였다. HILS 프로그램은 LabWindow/CVI를 이용하여 개발하였으며, 사용자는 선박의 현재 위치와 원하는 궤적을 모니터를 통해 본 후 조이스틱을 이용하여 선박의 전진 방향 및 회전방향 속도를 제어함으로서 선박을 조종한다. 시뮬레이션 결과를 보면 비선형 제어기와 PID 제어기는 개루프 조이스틱 제어기보다 타와 쓰러스터의 입력 크기뿐 아니라 선박의 위치오차 면에서도 우수한 성능을 보였다.

Along with the rapid growth of shipping and transportation , the size of a ship larger and larger. Low speed maneuverabililty of a full ship has been received a great deal of attention concerting about the navigation safety, especially in the harbour area of waterway. And, the iperation of the full ship in harbour area is one fo tehmost difficult technique. Usually highly experienced experts can make a suitable decision considering various propeller ,rudder actions and environmental conditions. The Artificial Neural Network is applied to the automatic berthing control of a ship. The teaching data are made by the berthing simulation of a ship on the computer. And, the layer neural network is used and the 'Error Back-Propagation Algorithm' is used to teach the neural network. Finally, it is shown that the berthing control is successfully done by the established neural network.

Studies on the ship's automatic navigation & berthing control have been continued by way of solving the ship's mathematical model, but the results of such studies have not reached to our satisfactory level due to its non-linear characteristics at low speed. In this paper, the authors propose a new berthing control system which can evaluate as closely as cap-tain's decision-making by using the FNN(Fuzzy Neural Network) controller which can simulate captain's knowledge. This berthing controller consists of the navigation subsystem FNN controller and the berthing subsystem FNN controller. The learning data are drawn from Ship Handling Simulator (NavSim NMS-90 MK Ⅲ) and represent the ship motion characteristics internally. According to learning procedure, both FNN controllers can tune membership functions and identify fuzzy control rules automatically. The verified results show the FNN controllers effective to incorporate captain's knowledge and experience of berthing.