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

본 연구는 미리 지정된 항로를 따라 항해하기 위한 선박의 Track-keeping에 대한 기초 실험 연구 분석결과를 제시하였다. 항로상에 위치한 여러 가지 변침점을 설정하고, 해당 선박이 그 항로를 항해하도록 알고리즘을 구성, 모형선을 이용한 실험 결과를 보여주었다. 지정된 구역에서 GPS로 변침점을 설정하고, 그 포인트를 Data화하여, 미리 프로그램된 알고리즘에 입력하면 해당 선박이 자동으로 항로를 항해하도록 설계되었다. PD 제어를 이용하여 침로 유지 알고리즘을 구성하였고, 선박 자동 Track-keeping 결과는 모니터링 가능토록 하였고, 변수를 설정 변경하도록 설계되었다. 제시된 알고리즘은 실험을 통하여 그 알고리즘의 효용성을 확인할 수 있었으며, 실선의 항해 자동화 및 충돌회피, 자동접안 등의 다양한 분야에 응용될 수 있을 것으로 판단된다.

This research presents an analysis of algorithm for ship track-keeping along a given trajectory. For the track-keeping problem , the m aneuver of w ay-point ship guiding through a sim ple path are presented. In order to solve the problem above, a desired trajectory is usually determ ined by G PS points in a pre-fixed place then these points are set in a pre-program m ed navigation that the ship w ill be autom atically tracked. Proportional-D erivative (PD ) control is useful for fast response controllers w ill be used in this program and the results of ship auto track-keeping experim ents will be explained in order to illustrate the adjustm ent in controlling param eters. These results can be utilized as a step to carry out the experim ent of ship collision avoidance system and autom atic berthing in the future.

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.

In Part I(theoretical study) of the paper, a new adaptive autopilot for ships based on Adaptive Neural Networks was proposed. The ANNAI autopilot was designed for course-keeping, turning and track-keeping control for ships. In this part of the paper, to show the effectiveness and feasibility of the ANNAI autopilot and automatic selection algorithm for learning rate and number of iterations, computer simulations of course-keeping and track-keeping tasks with and without the effects of measurement noise and external disturbances are presented. Additionally, the results of the previous studies using Adaptive Neural Network by backpropagation algorithm are also showed for comparison.

This paper presents a new adaptive autopilot for ships based on the Adaptive Neural Networks. The proposed adaptive autopilot is designed with some modifications and improvements from the previous studies on Adaptive Neural Networks by Adaptive Interaction (ANNAI) theory to perform course-keeping, turning and track-keeping control. A strategy for automatic selection of the neural network controller parameters is introduced to improve the adaptation ability and the robustness of new ANNAI autopilot. In Part II of the paper, to show the effectiveness and feasibility of the proposed ANNAI autopilot, computer simulations of course-keeping and track-keeping tasks with and without the effects of measurement noise and external disturbances will be presented.