• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.6
• /
• pp.592-600
• /
• 2010

The problem of motion control for AUV (Autonomous Underwater Vehicles) is addressed. The utilization of such robotic vehicles has gained an increasing importance in many marine activities. In this paper the objective is to describe how to design and apply FGS (Fuzzy Gain Scheduling) PD (Proportional Derivative) controller for an AUV (Autonomous Underwater Vehicle) to control the yaw and depth of the vehicle by keeping the path of the navigation to a desired point, and/or changing the path according to a set point.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.13 no.3
• /
• pp.154-163
• /
• 2013

In this paper, we propose a formation control algorithm for underactuated autonomous underwater vehicles (AUVs) with parametric uncertainties using the approach angle. The approach angle is used to solve the underactuated problem for AUVs, and the leader-follower strategy is used for the formation control. The proposed controller considers the nonzero off-diagonal terms of the mass matrix of the AUV model and the associated parametric uncertainties. Using the state transformation, the mass matrix, which has nonzero off-diagonal terms, is transformed into a diagonal matrix to simplify designing the control. To deal with the parametric uncertainties of the AUV model, a self-recurrent wavelet neural network is used. The proposed formation controller is designed based on the dynamic surface control technique. Some simulation results are presented to demonstrate the performance of the proposed control method.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.24 no.12
• /
• pp.2980-2988
• /
• 2000

In this paper, H(sub)$\infty$ depth and course controllers of autonomous underwater vehicles using H(sub)$\infty$ servo control are proposed. An H(sub)$\infty$ servo problem is foumulated to design the controllers satisfying a robust tracking property with modeling errors and disturbances. The solution of the H(sub)$\infty$servo problem is as follows; firest, this problem is modified as an H(sub)$\infty$ control problem for the generalized plant that includes a reference input mode, and than a sub-optimal solution that satisfies a given performance criteria is calculated by LMI(Linear Matrix Inequality) approach, The H(sub)$\infty$depth and course controllers are designed to satisfy the robust stability about the modeling error generated from the perturbation of the hydrodynamic coefficients and the robust tracking property under disturbances(was force, wave moment, tide). The performances(the robustness to the uncertainties, depth and course tracking properties) of the designed controlled are evaluated with computer simulations, and finally these simulation results show the usefulness and applicability of the propose H(sub)$\infty$ depth and course control systems.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.215-215
• /
• 2000

In this paper, depth and course controllers of autonomous underwater vehicles using H$_{\infty}$ servo control are proposed. An H$_{\infty}$ servo problem is formulated to design the controllers satisfying a robust tracking property with modeling errors and disturbances. The solution of the H$_{\infty}$ servo problem is as follows: first, this problem is modified as an H$_{\infty}$ control problem for the generalized plant that includes a reference input mode, and then a sub-optimal solution that satisfies a given performance criteria is calculated by LMI(Linear Matrix Inequality) approach. The H$_{\infty}$ depth and course controllers ate designed to satisfy with the robust stability about the modeling error generated from the perturbation of the hydrodynamic coefficients and the robust tracking property under disturbances(wave force, wave moment, tide). The performances(the robustness to the uncertainties, depth and course tracking properties) of the designed controllers are evaluated with computer simulations, and finally these simulation results show the usefulness and application of the proposed H$_{\infty}$ depth and course control systems.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.4 no.2
• /
• pp.135-141
• /
• 2004

This paper proposes a new heuristic search technique for obstacle avoidance of autonomous underwater vehicles equipped with a looking ahead obstacle avoidance sonar. We suggest the fuzzy relation between the sonar sections and the properties of real world environment. Bandler and Kohout's fuzzy relational method are used as the mathematical implementation for the analysis and synthesis of relations between the partitioned sections of sonar over the real-world environmental properties. The direction of the section with optimal characteristics would be selected as the successive heading of AUVs for obstacle avoidance. For the technique using in this paper, sonar range must be partitioned into multi equal sections; membership functions of the properties and the corresponding fuzzy rule bases are estimated heuristically. With the two properties Safety, Remoteness and sonar range partitioned in seven sections, this study gives the good result that enables AUVs to navigate through obstacles in the optimal way to goal.

• Journal of Positioning, Navigation, and Timing
• /
• v.12 no.1
• /
• pp.67-73
• /
• 2023

As interest in underwater structures and ocean exploration increases, many researchers are proposing methods for modeling and controlling various remotely operated vehicles (ROVs). Recently, hybrid systems composed of an autonomous underwater vehicle and an ROV capable of remote control and autonomous navigation are being developed. In this study we introduce a method that models Ariari-aROV, an ROV consisting of five thrusters, and performs navigation. The proposed ROV can be controlled manually and by autonomous navigation when given a target point. An extended Kalman filter is utilized for sensor measurement correction for more precise navigation. The proposed method is verified through a simulation.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.12 no.1
• /
• pp.1-10
• /
• 2020

This paper investigates the influence of the Circulating Water Channel (CWC) side wall and support struts on the hydrodynamic coefficient prediction for Autonomous Underwater Vehicles (AUVs) experiments. Computational Fluid Dynamics (CFD) method has been used to model the CWC tests. The hydrodynamic coefficients estimated by CFD are compared with the prediction of experiments to verify the accuracy of simulations. In order to study the effect of side wall on the hydrodynamic characteristics of the AUV in full scale captive model tests, this paper uses the CWC non-dimensional width parameters to quantify the correlation between the CWC width and hydrodynamic coefficients of the chosen model. The result shows that the hydrodynamic coefficients tend to be constant with the CWC width parameters increasing. Moreover, the side wall has a greater effect than the struts.

• International Journal of Control, Automation, and Systems
• /
• v.2 no.3
• /
• pp.374-383
• /
• 2004

This paper presents a neural network adaptive controller for autonomous diving control of an autonomous underwater vehicle (AUV) using adaptive backstepping method. In general, the dynamics of underwater robotics vehicles (URVs) are highly nonlinear and the hydrodynamic coefficients of vehicles are difficult to be accurately determined a priori because of variations of these coefficients with different operating conditions. In this paper, the smooth unknown dynamics of a vehicle is approximated by a neural network, and the remaining unstructured uncertainties, such as disturbances and unmodeled dynamics, are assumed to be unbounded, although they still satisfy certain growth conditions characterized by 'bounding functions' composed of known functions multiplied by unknown constants. Under certain relaxed assumptions pertaining to the control gain functions, the proposed control scheme can guarantee that all the signals in the closed-loop system satisfy to be uniformly ultimately bounded (UUB). Simulation studies are included to illustrate the effectiveness of the proposed control scheme, and some practical features of the control laws are also discussed.

• Journal of the Society of Naval Architects of Korea
• /
• v.61 no.4
• /
• pp.216-225
• /
• 2024

The sliding mode controller has characteristics that ensure stability and robustness against system uncertainty and disturbance. However, chattering occurs in the control inputs to compensate for system uncertainties and ensure that the system operates efficiently on the sliding surface. When the disturbance is large, using a sliding mode controller requires a large control gain value, which also increases chattering and reduces performance. Therefore, in this study, a nonlinear disturbance observer was used to compensate for external disturbances such as currents and waves and uncertainty in the control system for autonomous underwater vehicles. Accordingly, a robust controller can be implemented while reducing the control gain. The disturbance observer serves to ensure that the behavior of the actual system is closer to the nominal model by compensating for uncertainties between the actual system model and the nominal model during the control process. Therefore, the simulation results show that the performance and robustness of the autonomous underwater vehicle controller are improved by introducing a disturbance observer.

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