The research vessel NARA equipped with an azimuth thruster system was built in 2015. There are few vessels with this propulsion system in Korea. This vessel has two modes such as the normal for maneuvering and the power for investigation, and the other two modes as one axis and two axes on the operating. This type of vessels does not seem to have a clear grasp of the maneuvering character in comparison with the vessel with a conventional propulsion system. So the authors carried out the sea test for the turning, the zigzag and the inclination, and the results are as follows. In turning test, the case of using the two axes mode is much better than the case of using the one axis mode for the elements of turning, such as advance, transfer, tactical diameter and final diameter, but turning hard over the rudder in full speed is very vulnerable to capsize in both modes. In zigzag test, the yaw quicking responsibility index,  is very large excessively, which means a bad counter maneuvering ability, so an operator has to keep in mind that in turning operation. If necessary to avoid collision at head on situation, it may be a more effective method to use the crash astern stop than the turning according to the conditions and circumstances for the shortest stopping distance is very short.

It is indispensable to grasp the turning ability of a ship to operate her effectively. For this purpose, the author measured the turning ability of training ship, A-RA by use of bow thruster and stem rudder. The turning ability of this ship, in case of using both of stem rudder and bow thruster at the same time, caused by increase of steering angle provides more influence to the size of tactical diameter than it caused by the power of bow thruster. But the influence of bow thruster on the turning ability is available only within rudder angle 5˚ - 10˚, so it is possible to grasp that the effect of bow truster is reduced as rudder angle become bigger. In case of the influence of bow thruster by her speed, the ability of bow thruster is very effective at low speed, but it is almost not available in normal turning speed. Therefore, the using both of stem rudder and bow thruster can be useful in case of low speed proceeding at entrance or departure of the narrow waterway or inside port which sea traffic is congest for collision avoidance.

Underwater robotic vehicles(URVs) are used for various work assignments such as pipe-lining, inspection, data collection, drill support, hydrography mapping, construction, maintenance and repairing of undersea equipment, etc. As the use of such vehicles increases the development of vehicles having greater autonomy becomes highly desirable. The vehicle control system is one of the most critic vehicle subsystems to increase autonomy of the vehicle. The vehicle dynamics is nonlinear and time-varying. Hydrodynamic coefficients are often difficult to accurately estimate. It was also observed by experiments that the effect of electrically powered thruster dynamics on the vehicle become significant at low speed or stationkeeping. The conventional linear controller with fixed gains based on the simplified vehicle dynamics, such as PID, may not be able to handle these properties and result in poor performance. Therefore, it is desirable to have a control system with the capability of learning and adapting to the changes in the vehicle dynamics and operating parameters and providing desired performance. This paper presents an adaptive and learning control system which estimates a new set of parameters defined as combinations of unknown bounded constants of system parameter matrices, rather than system parameters. The control system is described with the proof of stability and the effect of unmodeled thruster dynamics on a single thruster vehicle system is also investigated.

The thruster is the crucial factor of an underwater vehicle system, because it is the lowest layer in the control loop of the system. In this paper, we propose an accurate and practical thrust modeling for underwater vehicles which considers the effects of ambient flow velocity and angle. In this model, the axial flow velocity of the thruster, which is non-measurable, is represented by ambient flow velocity and propeller shaft velocity. Hence, contrary to previous models, the proposed model is practical since it uses only measurable states. Next, the whole thrust map is divided into three states according to the state of ambient flow and propeller shaft velocity, and one of the borders of the states is defined as Critical Advance Ratio (CAR). This classification explains the physical phenomenon of conventional experimental thrust maps. In addition, the effect of the incoming angle of ambient flow is analyzed, and Critical Incoming Angle (CIA) is also defined to describe the thrust force states. The proposed model is evaluated by comparing experimental data with numerical model simulation data, and it accurately covers overall flow conditions within 2N force error. The comparison results show that the new model's matching performance is significantly better than conventional models'.