• Journal of Ocean Engineering and Technology
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• v.8 no.2
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• pp.131-140
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• 1994

Dynamics of AUV has heavy nonlinearities and many unknown parameters due to its bluff shape and low cruising speed. Intelligent algorithms, therefore, are required to overcome these nonlinearities and unknown system dynamics. Several identification techniques have been suggested for the application of control of underwater vehicles during last decade. This paper applies the neural network to identification and motion control problem of AUVs. Nonlinear dynamic systems of an AUV are identified using feedforward neural network. Simulation results show that the learned neural network can generate the motion of AUV. This paper, also, suggest an adaptive control scheme up-dates the controller weights with reference model and feedforward neural network using error back propagation.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.6
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• pp.784-790
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• 2007

This paper is mainly concerned with the development of the semi-autonomous underwater vehicle (SAUV). Underwater vehicles are affected by external disturbances due to the sea conditions such as currents and waves when it is performing various missions In this paper we present a design scheme of the SAUV system with mathematical models. Also. we present a control system including motion control of motors and main controller and a communication based on CAN method for interrelated control between the controllers and actuators.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.227-237
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• 1997

Autonomous Underwater Vehicles(AUVs) have become an important tool for various purposes in subsea: inspection, recovery, construction, etc., and the development of autonomous control system is luglay desirable- thete zffe many problems associated with designing the control system for AUV due to unknown underwater envimn-Tnent, the possibility of subsystem failures, and unpredictable changes in the dynamics of the vehicle. In this paper, an autonomous control system based on the intelligent control theory to enhance operation efficiency of the ALTV is presented. The control system has a hierarchical structure which consists of mission planning level, mission control level, navigation level, and execution level. The performance of the control system is investigated by computer simulation. The results show that the proposed control system can be applied successfully to the AUV in spite of the possibility of failures in the vehicle and the collision hazard in the sea environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.233-239
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• 2019

An unmanned underwater vehicle is a major weapon system that allows surveillance and reconnaissance missions in border areas or threatening areas where enemy submarines are present. Unmanned underwater vehicles can be used to explore underwater resources, predict disasters, and survey the topography of the ocean floor in the civilian fields, while in the defense fields, it can be used for anti-submarine reconnaissance and mine countermeasures. In this paper, we first investigate the main classification of unmanned underwater vehicles, and foreign R&D trends are analyzed based on the main classification criteria by weight, such as portable, light, heavy and large-scale unmanned underwater vehicles. Then we examine the trends in the development of domestic unmanned underwater vehicles. Finally, through the analysis of both domestic and foreign unmanned underwater vehicles, we present future development trends of unmanned underwater vehicles in order to set defense goals to counter the anticipated threats and diversified potential environment.

• Journal of Ocean Engineering and Technology
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• v.17 no.1
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• pp.1-7
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• 2003

Autonomous underwater vehicles (AUVs) are unmanned, underwater vessels that are used to investigate sea environments in the study of oceanography. Docking systems are required to increase the capability of the AUVs, to recharge the batteries, and to transmit data in real time for specific underwater works, such as repented jobs at sea bed. This paper presents a visual :em control system used to dock an AUV into an underwater station. A camera mounted at the now center of the AUV is used to guide the AUV into dock. To create the visual servo control system, this paper derives an optical flow model of a camera, where the projected motions of the image plane are described with the rotational and translational velocities of the AUV. This paper combines the optical flow equation of the camera with the AUVs equation of motion, and deriver a state equation for the visual servo AUV. Further, this paper proposes a discrete-time MIMO controller, minimizing a cost function. The control inputs of the AUV are automatically generated with the projected target position on the CCD plane of the camera and with the AUVs motion. To demonstrate the effectiveness of the modeling and the control law of the visual servo AUV simulations on docking the AUV to a target station are performed with the 6-dof nonlinear equations of REMUS AUV and a CCD camera.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.82-86
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• 2008

The article presents ARTMAP and Fuzzy BK-Product approach underwater obstacle avoidance for the Autonomous underwater Vehicles (AUV). The AUV moves an unstructured area of underwater and could be met with obstacles in its way. The AUVs are equipped with complex sensorial systems like camera, aquatic sonar system, and transducers. A Neural integrated Fuzzy BK-Product controller, which integrates Fuzzy logic representation of the human thinking procedure with the learning capabilities of neural-networks (ARTMAP), is developed for obstacle avoidance in the case of unstructured areas. In this paper, ARTMAP-Fuzzy BK-Product controller architecture comprises of two distinct elements, are 1) Fuzzy Logic Membership Function and 2) Feed-Forward ART component. Feed-Forward ART component is used to understanding the unstructured underwater environment and Fuzzy BK-Product interpolates the Fuzzy rule set and after the defuzzyfication, the output is used to take the decision for safety direction to go for avoiding the obstacle collision with the AUV. An on-line reinforcement learning method is introduced which adapts the performance of the fuzzy units continuously to any changes in the environment and make decision for the optimal path from source to destination.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.109.5-109
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• 2002

Autonomous underwater vehicles (AUVs) are unmanned underwater vessels to investigate sea environments, oceanography and deep-sea resources autonomously. Docking systems are required to increase the capability of the AUVs to recharge the batteries and to transmit data in real time in underwater. This paper presents a visual servo control system for an AUV to dock into an underwater station with a camera. To make the visual servo control system , this paper derives an optical flow model of a camera mounted on an AUV, where a CCD camera is installed at the nose center of the AUV to monitor the docking condition. This paper combines the optical flow equation of the camera with the AUV's equation o...

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.335-335
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• 2000

Applications such as unmanned aerial vehicles (UAVs), autonomous underwater vehicles (AUVs) and the time varying nature of their navigation, guidance and control systems motivate an integrated approach to trajectory general ion and trajectory tracking for autonomous vehicles. In this paper, an experimental testbed was designed for studying this integrated trajectory control approach. In this paper we apply the separating approach to an autonomous nonlinear vehicle system. A new linear matrix inequality based H$_{\infty}$ control technique for periodic time-varying systems is applied to the role of trajectory tracking. Trajectory general ion is accomplished by exploit ing the differential flatness property of the vehicle system; this at lows product ion of desired feasible nominal or reference trajectories from certain ″flat'system outputs. Simulation and experimental results are presented showing stable tracking of a periodic circular trajectory.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.406-413
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• 2007

This paper proposes a self-recurrent wavelet neural network(SRWNN) based adaptive backstepping control technique for the robust steering control of autonomous underwater vehicles(AUVs) with unknown model uncertainties and external disturbance. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the steering model of AUV. The adaptation laws for the weights of SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for the on-line control of AUV. Finally, simulation results for steering control of an AUV with unknown model uncertainties and external disturbance are included to illustrate the effectiveness of the proposed method.

• Journal of Ocean Engineering and Technology
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• v.36 no.5
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• pp.340-352
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• 2022

This article presents a modeling method for the uncorrelated measurement error of the ultra-short baseline (USBL) acoustic positioning system for aiding navigation of underwater vehicles. The Mahalanobis distance (MD) and principal component analysis are applied to decorrelate the errors of USBL measurements, which are correlated in the x- and y-directions and vary according to the relative direction and distance between a reference station and the underwater vehicles. The proposed method can decouple the radial-direction error and angular direction error from each USBL measurement, where the former and latter are independent and dependent, respectively, of the distance between the reference station and the vehicle. With the decorrelation of the USBL errors along the trajectory of the vehicles in every time step, the proposed method can reduce the threshold of the outlier decision level. To demonstrate the effectiveness of the proposed method, simulation studies were performed with motion data obtained from a field experiment involving an autonomous underwater vehicle and USBL signals generated numerically by matching the specifications of a specific USBL with the data of a global positioning system. The simulations indicated that the navigation system is more robust in rejecting outliers of the USBL measurements than conventional ones. In addition, it was shown that the erroneous estimation of the navigation system after a long USBL blackout can converge to the true states using the MD of the USBL measurements. The navigation systems using the uncorrelated error model of the USBL, therefore, can effectively eliminate USBL outliers without loss of uncontaminated signals.