Identification of hydrodynamic coefficients of AUV in the presence of measurement biases

2021 ◽  
pp. 147509022110574
Author(s):  
Mustafa Dinç ◽  
Chingiz Hajiyev
Keyword(s):  
Parameter Identification ◽  
Navigation System ◽  
Autonomous Underwater Vehicle ◽  
Least Square ◽  
Parameter Determination ◽  
Doppler Velocity ◽  
Second Phase ◽  
Hydrodynamic Coefficients ◽  
Identification Method ◽  
Measurement Biases

This paper mainly presents the parameter identification method developed from a Least Square Estimation (LSE) algorithm to estimate hydrodynamic coefficients of Autonomous Underwater Vehicle (AUV) in the presence of measurement biases. LSE based parameter determination method is developed to obtain unbiased estimated values of hydrodynamic coefficients of AUV from biased Inertial Navigation System (INS) measurements. The proposed parameter identification method consists of two phases: in the first phase, high precision INS and its auxiliary instrument including compass, pressure depth sensor, and Doppler Velocity Log (DVL) are designed as Integrated Navigational System coupled with Complementary Kalman Filter (CKF) to determine hydrodynamic coefficients of AUV by removing the INS measurement biases; in the second phase, LSE based parameter identification method is applied to the model in the first phase for obtaining unbiased estimated values of hydrodynamic coefficients of AUV. In this paper, a method for identifying the yaw and sway motion dynamic parameters of an AUV is given. Various maneuvering scenarios are verified to assess the parameter identification method employed. The simulation results indicate that using the CKF based Integrated Navigation System together with unbiased measurement conversion could produce better results for estimating the hydrodynamic coefficients of AUV.

A UAV MODEL PARAMETER IDENTIFICATION METHOD: A SIMULATION STUDY

2009 ◽  
Vol 06 (04) ◽  
pp. 225-238 ◽  
Author(s):  
K. S. HATAMLEH ◽  
O. MA ◽  
R. PAZ
Keyword(s):  
Parameter Identification ◽  
Control Strategies ◽  
Least Square ◽  
Model Parameters ◽  
Dynamics Modeling ◽  
Recursive Least Square ◽  
Nonlinear Dynamical ◽  
Model Parameter ◽  
Identification Method ◽  
Model Parameter Identification

Dynamics modeling of Unmanned Aerial Vehicles (UAVs) is an essential step for design and evaluation of an UAV system. Many advanced control strategies for nonlinear dynamical or robotic systems which are applicable to UAVs depend upon known dynamics models. The accuracy of a model depends not only on the mathematical formulae or computational algorithm of the model but also on the values of model parameters. Many model parameters are very difficult to measure for a given UAV. This paper presents the results of a simulation based study of an in-flight model parameter identification method. Assuming the motion state of a flying UAV is directly or indirectly measureable, the method can identify the unknown inertia parameters of the UAV. Using the recursive least-square technique, the method is capable of updating the model parameters of the UAV while the vehicle is in flight. A scheme of estimating an upper bound of the identification error in terms of the input data errors (or sensor errors) is also discussed.

scholarly journals NAVIGATION AND MOTION CONTROL SYSTEMS OF THE AUTONOMOUS UNDERWATER VEHICLE

2020 ◽  
Vol 4 ◽  
pp. 38-50
Author(s):  
Dmitry Antonov ◽  
Leonid Kolganov ◽  
Aleksey Savkin ◽  
Egor Chekhov ◽  
Maxim Ryabinkin
Keyword(s):  
Control System ◽  
Automatic Control ◽  
Control Systems ◽  
Motion Control ◽  
Navigation System ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Transportation Safety ◽  
Doppler Velocity ◽  
Using Data

Autonomous underwater vehicles (AUVs) are widely used and have proven their effectiveness in tasks such as transportation safety, area monitoring and seafloor mapping. When developing AUV’s navigation and control systems, the engineers have to ensure the required levels of accuracy and reliability for solving navigation and motion control tasks in autonomous underwater operation under restrictions on the overall dimensions and power consumption of the AUV. The main purpose of this paper is to present preliminary results of AUV navigation and motion control systems development. The AUV’s navigation system is built around strapdown inertial navigation system (SINS) designed specifically for this AUV. When surfaced, position and angular SINS correction is performed using data from dual-antenna GNSS receiver and doppler velocity log (DVL). When underwater, SINS position and velocity correction is performed using acoustic navigation system (ANS) and DVL data. AUV’s control system provides manual and automatic control. Manual control is carried out in real-time by operator via fiber-optic cable using a joystick. Automatic control allows AUV to move independently along a specified trajectory at a given depth and speed. The AUV also has a collision avoidance system that utilizes readings from a forward-facing acoustic rangefinder to estimate time before impact based on AUV’s analytic model. If possible collision is detected, information is transmitted to the control system so that a further appropriate action can be taken. Computer simulation utilizing the analytic AUV model was used in order to check the performance characteristics of the designed control and navigation algorithms. After confirming the operability of the developed algorithms, preliminary tests of the AUV were carried out. During the tests, AUV’s on-board equipment and navigation system readings were recorded and compared to the readings of the reference system, which was also installed on the AUV. During the tests, the dynamic characteristics of the AUV were evaluated. AUV’s characteristics obtained during simulation and testing will be used as a reference during future development

A Low Complexity Integrated Navigation System for Underwater Vehicles

2018 ◽  
Vol 71 (5) ◽  
pp. 1161-1177 ◽  
Author(s):  
Mehdi Emami ◽  
Mohammad Reza Taban
Keyword(s):  
Navigation System ◽  
Autonomous Underwater Vehicle ◽  
Three Dimensional ◽  
Low Complexity ◽  
Doppler Velocity ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Sea Trial ◽  
Integrated Navigation System ◽  
Computational Load

This paper proposes a simplified algorithm for reducing the computational load of the conventional underwater integrated navigation system. The system usually comprises a three-dimensional accelerometer, a three-dimensional gyroscope, a three-dimensional Doppler Velocity Log (DVL) and a data fusion algorithm, such as a Kalman Filter (KF). Since the expected variations of roll, pitch and depth are small, these quantities are assumed to be constant, and the proposed system is designed in a two-dimensional form. Due to the low speed of the vehicle, the nonlinear dynamic equation of the velocity can be simplified in a linear form. We also simplify the conventional KF in order to avoid matrix multiplications and matrix inversions. The performance of the designed system is evaluated in a sea trial by an Autonomous Underwater Vehicle (AUV). The results show that the proposed system can significantly reduce the computational load of the conventional integrated navigation system without a significant reduction in position and velocity accuracy.

scholarly journals Energy Modeling and Parameter Identification of Dual-Motor-Driven Belt Conveyors without Speed Sensors

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3313 ◽  
Author(s):  
Chunyu Yang ◽  
Jinhao Liu ◽  
Heng Li ◽  
Linna Zhou
Keyword(s):  
Parameter Identification ◽  
Energy Model ◽  
Least Square ◽  
Recursive Least Square ◽  
Extended Kalman Filtering ◽  
Identification Method ◽  
Driven System ◽  
New Energy ◽  
Least Square Approach ◽  
Belt Conveyors

The energy model of belt conveyors plays a key role in the energy efficiency optimization problem of belt conveyors. However, the existing energy models and parameter identification methods are mainly limited to single-motor-driven belt conveyors and require speed sensors. This paper will present an energy model and a parameter identification method for dual-motor-driven belt conveyors whose speed sensors are not available. Firstly, a new energy model of dual-motor-driven belt conveyors is established by combining the traditional energy model with the dynamic model of a dual-motor-driven system. Then, a parameter identification method based on an extended Kalman filtering algorithm and recursive least square approach is proposed. Finally, the feasibility and effectiveness of the method are demonstrated by simulation experiments.

Conceptual Design of Navigation of an AUV for Monitoring CCS Site at Deep Sea Bottom

2011 ◽  
Author(s):  
Yoshitaka Watanabe ◽  
Hiroshi Yoshida ◽  
Hiroshi Ochi ◽  
Tadahiro Hyakudome ◽  
Shojiro Ishibashi ◽  
...  
Keyword(s):  
Conceptual Design ◽  
Navigation System ◽  
Autonomous Underwater Vehicle ◽  
Acoustic Method ◽  
Doppler Velocity ◽  
Depth Sensor ◽  
Short Baseline ◽  
Position Information ◽  
Sea Bottom ◽  
Acoustic Methods

We, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), are developing an autonomous underwater vehicle (AUV) whose main mission is monitoring a site at the sea bottom for the carbon dioxide capture and storage (CCS). The AUV cruises very near the sea bottom, and is equipped with chemical sensors in order to detect escape of CO2 from sub-bottom. Of course, the position information of the AUV is critical information for the monitoring. In this paper, a conceptual design of navigation of the AUV is described. Recently, navigation of AUV is implemented by integrating multiple navigation devices including inertial navigation system (INS), Doppler velocity log (DVL), depth sensor, acoustic navigation system, and others. The AUV under construction will be equipped with these navigation sensors, and will integrate those sensors’ outputs to navigate herself. In order to measure the absolute position of the AUV the acoustic method is one of fundamental technique. At the first step of development of the AUV, three acoustic methods are considered to adopt. The three methods are super short baseline (SSBL) method which is a tracking from support ship or other surface station, long baseline (LBL) which is navigation based on preplaced acoustic transponders, and virtual LBL (VLBL) which is navigation based on only single transponder. These acoustic methods are integrated with the navigation result of INS, depth sensor, and DVL. The three methods are used in each appropriate case. Which feature of observation is desired simplicity, accuracy, or independence from support ship and time efficiency? The acoustic method is influenced by environment, and also output of other sensors is depending on the environment, for example the DVL miss the data when the terrain is with many up-hills and down-hills. The integration or filtering parameters of the navigation should be adjusted depending on the influential environmental factor.

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