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

Mustafa Dinç*, Chingiz Hajiyev

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)756-763
Number of pages8
JournalProceedings of the Institution of Mechanical Engineers Part M: Journal of Engineering for the Maritime Environment
Volume236
Issue number3
DOIs
Publication statusPublished - Aug 2022

Bibliographical note

Publisher Copyright:
© IMechE 2021.

Keywords

  • integrated navigational system
  • Kalman filter
  • least square estimation
  • measurement bias
  • modeling of autonomous underwater vehicle
  • Parameter identification

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