Depth and heading control for autonomous underwater vehicle using estimated hydrodynamic coefficients

2002 ◽  
Author(s):  
Joonyoung Kim ◽  
Kihun Kim ◽  
H.S. Choi ◽  
Woojae Seong ◽  
Kyu-Yeul Lee
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Hydrodynamic Coefficients ◽  
Heading Control

Developing data fusion and recursive estimation methods for online identification of dive plane dynamics of an autonomous underwater vehicle

2019 ◽  
Vol 234 (2) ◽  
pp. 520-533
Author(s):  
Seid Farhad Abtahi ◽  
Mohammad Mehdi Alishahi ◽  
Ehsan Azadi Yazdi
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Unscented Kalman Filter ◽  
Underwater Vehicle ◽  
Recursive Estimation ◽  
High Rate ◽  
Pole Placement ◽  
Estimation Methods ◽  
Recursive Identification ◽  
Identification Algorithm ◽  
Hydrodynamic Coefficients

The purpose of this article is to develop an online method to identify the hydrodynamic coefficients of pitch plane of an autonomous underwater vehicle. To obtain necessary data for the identification, the dive plane dynamics should be excited through diving maneuvers. Hence, a controller is needed whose performance and stability are appropriate. To design such a controller, first, hydrodynamic coefficients are approximated using semi-empirical methods. Based on these approximated coefficients, a classic controller is designed at the next step. Since the estimation of these coefficients is uncertain, µ-analysis is employed to verify the robustness of stability and performance of the controller. Using the verified robust controller, some oscillating maneuvers are carried out that excite the dive plane dynamics. Using sensor fusion and unscented Kalman filter, smooth and high-rate data of depth is provided for the depth controller. A recursive identification algorithm is developed to identify the hydrodynamic coefficients of heave and pitch motions. It turns out that some inputs required by the identification are not measured directly by the sensors. But the devised fusion algorithm is able to provide the necessary data for identification. Finally, using the identified coefficients and employing pole placement method, a new controller with better performance is synthesized online. To evaluate the performance of the identification and fusion algorithms, a 6-degree-of-freedom simulation of an autonomous underwater vehicle is carried out.

scholarly journals Design, Analysis and Development of Autonomous Underwater vehicle

2019 ◽  
Vol 9 (2) ◽  
pp. 3805-3810
Keyword(s):  
Object Recognition ◽  
Motion Control ◽  
Water Surface ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Heading Control ◽  
Underwater Object ◽  
Institute Of Technology

Autonomous Underwater Vehicles (AUV) are slowly operated unmanned robots which Capable of propelling on pre-defined mission tracks independently under the water surface and are frequently used for oceanographic exploration, bathymetric surveys and defense applications. This AUV can perform underwater object recognition and obstacle avoidance with the use of appropriate sensors and devices. Vidyut is a miniature AUV developed at Sri Sairam Institute of Technology. The vehicle is equipped with six thrusters which allow for motion control in 6 Dof and has a non-conventional single hull heavy bottom hydrodynamic design. This paper discusses different aspects of the vehicle's unique design. The output of the Arduino Uno controller has been discussed for continuous depth and heading control.

Identification of Underwater Vehicle Hydrodynamic Coefficients Using Model Tests

2010 ◽  
Author(s):  
B. Sadeghzadeh ◽  
H. Mehdigholi
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Controller Design ◽  
Underwater Vehicle ◽  
Model Tests ◽  
Vehicle Design ◽  
Hydrodynamic Coefficients ◽  
Test Results ◽  
Engineering Research ◽  
University Of Technology ◽  
Marine Engineering

Predicting the hydrodynamic coefficients of an autonomous underwater vehicle (AUV) is important during vehicle design. SUT-2 is an AUV, being developed by the Marine Engineering Research Center of Sharif University of Technology in Iran (MERC). Model tests are done in the marine engineering laboratory towing tank. In this research, hydrodynamic coefficients are calculated using model test results of an autonomous underwater vehicle. Hydrodynamic forces are also analyzed. These coefficients are used for dynamic modeling and autonomous controller design.

scholarly journals Visual feedback–based heading control of autonomous underwater vehicle for pipeline corrosion inspection

2017 ◽  
Vol 14 (3) ◽  
pp. 172988141665817 ◽  
Author(s):  
Amjad Khan ◽  
Syed Saad Azhar Ali ◽  
Fabrice Meriaudeau ◽  
Aamir Saeed Malik ◽  
Lim Sheng Soon ◽  
...  
Keyword(s):  
Visual Feedback ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Heading Control ◽  
Pipeline Corrosion

scholarly journals Neural Network Predictive Control for Autonomous Underwater Vehicle with Input Delay

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Jiemei Zhao
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Control Algorithm ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Path Tracking ◽  
Input Delay ◽  
Hydrodynamic Coefficients ◽  
Lyapunov Theorem ◽  
Neural Network Predictive Control

A path tracking controller is designed for an autonomous underwater vehicle (AUV) with input delay based on neural network (NN) predictive control algorithm. To compensate for the time-delay in control system and realize the purpose of path tracking, a predictive control algorithm is proposed. An NN is used to estimate the nonlinear uncertainty of AUV induced by hydrodynamic coefficients and the coupling of the surge, sway, and yaw angular velocity. By Lyapunov theorem, stability analysis is also given. Simulation results show the effectiveness of the proposed control strategy.

Estimation of the hydrodynamic coefficients of the complex-shaped autonomous underwater vehicle TUNA-SAND

2009 ◽  
Vol 14 (3) ◽  
pp. 373-386 ◽  
Author(s):  
Sulin Tang ◽  
Tamaki Ura ◽  
Takeshi Nakatani ◽  
Blair Thornton ◽  
Tao Jiang
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Hydrodynamic Coefficients

scholarly journals Soft-Switching Proximate Time Optimal Heading Control for Underactuated Autonomous Underwater Vehicle

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 143233-143249 ◽  
Author(s):  
An Li ◽  
Li Ye ◽  
Jiang Yanqing ◽  
Li Yueming ◽  
Cao Jian ◽  
...  
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Soft Switching ◽  
Heading Control ◽  
Time Optimal

Identification of roll dynamics of an autonomous underwater vehicle

2018 ◽  
Vol 233 (4) ◽  
pp. 1056-1067 ◽  
Author(s):  
Seid Farhad Abtahi ◽  
Mohammad Mehdi Alishahi ◽  
Ehsan Azadi Yazdi
Keyword(s):  
Degrees Of Freedom ◽  
Autonomous Underwater Vehicle ◽  
Optimal Solution ◽  
Underwater Vehicle ◽  
Hydrodynamic Coefficients ◽  
Proportional Integral Derivative ◽  
Unknown Parameters ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
And Performance

This article aims to identify the roll channel parameters of an autonomous underwater vehicle. These parameters include hydrodynamic coefficients, motor torque, eccentricity, misalignments and mounting imperfections. In the proposed method, an approximation of the hydrodynamic coefficients is made at first via semi-empirical methods. In the next step, a proportional–integral–derivative controller is designed with respect to the approximated coefficients. Since the approximations can be very uncertain, the robustness of stability and performance of the proportional–integral–derivative controller is evaluated throughout µ-analysis. Finally, the unknown parameters are identified using the recorded data of on-board sensors during motion of the vehicle. The identification is based on minimization of the one-step prediction error. The minimization problem is nonlinear in unknown parameters, and particle swarm optimization is used to find an optimal solution. The performance of the proposed method is exhibited through a 6-degrees-of-freedom simulation of an autonomous underwater vehicle.

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