Skip to main content
SpringerLink
Log in

Estimation of hydrodynamic parameters for underwater systems using a simple off-line regression method: a case study

  • Original article
  • Published:
Journal of Marine Science and Technology Aims and scope Submit manuscript

Abstract

Estimation of hydrodynamic model parameters such as added mass parameter and drag coefficients is very crucial while mathematically modelling any underwater system. Owing to the nature of the model being coupled and its high non-linearity, estimation of these parameters becomes complicated. Various methods of parameter estimation have been currently employed, involving experiments, computational fluid analysis, and simulations, each having its own advantages and disadvantages. Most of the existing methods use sophisticated external instrumentation and experimentally estimate the parameters using computationally expensive adaptive algorithms, which may not be required or cannot be generalised to all the systems. In this paper, a simple off-line estimation method for estimating crucial hydrodynamic parameters using onboard sensors is presented. The error between the data from numerical simulations (using a mathematical model) and experiments is iteratively minimised using a gradient descent-based optimisation algorithm, by having the unknown model parameters as design variables of the error minimisation process. The method combines the properties of least squares estimation and the free decay tests, where the system can be excited with any known external inputs. A mathematical model with the unknown parameters, fully defining the behaviour of the system, is required and open loop experimental data from onboard sensors for a known input is sufficient for the estimation process, thereby eliminating the requirement of additional instrumentation. Non-linear mathematical models can be directly used in estimation, unlike few other methods which require linearisation and approximation. This method can be generalised to any system, provided sufficient information on experimental input and output, and the equivalent mathematical model of the system are available. The proposed method has been successfully implemented to estimate the added mass and drag coefficients of a standalone, single degree of freedom variable buoyancy module, ‘vBuoy’. A mathematical model defining the dynamics of the heave motion of vBuoy has been derived and the parameters involved in the model have been estimated with the proposed method. The proposed method, as well as the results, are validated by comparing the experiments and simulations at different conditions. The results showed that the proposed method was well suited for the estimation of hydrodynamic parameters of underwater systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Shibuya K, Kawai K (2009) Development of a new buoyancy control device for underwater vehicles inspired by the sperm whale hypothesis. Adv Robot 23(7–8):831–846

    Google Scholar 

  2. Shibuya K, Yoshii S (2013) New volume change mechanism using metal bellows for buoyancy control device of underwater robots. Int Sch Res Not Robot, 1–7

  3. Tangirala S, Dzielski J (2007) A variable buoyancy control system for a large AUV. IEEE J Ocean Eng 32(4):762–771

    Article  Google Scholar 

  4. Wu PK et al (2011) Zero-power autonomous buoyancy system controlled by microbial gas production. Rev Sci Instrum 82(5):55108

    Article  Google Scholar 

  5. Worall M, Jamieson AJ, Holford A, Neilson RD (2007) A variable buoyancy system for deep ocean vehicles. In: Proceedings of OCEANS 2007, vol 44. pp 1–6

  6. De Zhao W, Xu JA, Zhang MJ (2010) A variable buoyancy system for long cruising range AUV. In: Proceedings of international conference on computer, mechatronics, control and electronic engineering, CMCE 2010, vol 2. pp 585–588

  7. Ranganathan T, Thondiyath A (2016) Design and analysis of cascaded variable buoyancy systems for selective underwater deployment. In: Proceedings of the 13th international conference on informatics in control, automation and robotics (ICINCO 2016), vol 2. pp. 319–326

  8. Ranganathan T, Singh V, Nair R, Thondiyath A (2017) Design of a controllable variable buoyancy module and its performance analysis as a cascaded system for selective underwater deployment. Proc Inst Mech Eng Part M J Eng Marit Environ 231(4):888–901

    Google Scholar 

  9. Jagadeesh P, Murali K, Idichandy VG (2009) Experimental investigation of hydrodynamic force coefficients over AUV hull form. Ocean Eng 36(1):113–118

    Article  Google Scholar 

  10. Listak M, Pugal D, Kruusmaa M (2008) CFD simulations and real world measurements of drag of biologically inspired underwater robot. In: Proceedings of US/EU-baltic international symposium: ocean observations, ecosystem-based management and forecasting

  11. Eng YH, Chin CS, Lau MWS (2014) Added mass computation for control of an open-frame remotely-operated vehicle: application using WAMIT and MATLAB. J Mar Sci Technol 22(4):405–416

    Google Scholar 

  12. Chin C, Lau M (2012) Modeling and testing of hydrodynamic damping model for a complex-shaped remotely-operated vehicle for control. J Mar Sci Appl 11(2):150–163

    Article  Google Scholar 

  13. Fernandes AC, Mineiro FPS (2007) Assessment of hydrodynamic properties of bodies with complex shapes. Appl Ocean Res 29(3):155–166

    Article  Google Scholar 

  14. Aage A, Wagner Smitt L (1994) Hydrodynamic manoeuvrability data of a flatfish type autonomous underwater vehicle. In: Proceedings of OCEANS 1994, vol 3. pp. 425–430

  15. Morrison TI, Yoerger DR (1993) Determination of the hydrodynamic parameters of an underwater vehicle during small scale, nonuniform, 1-dimensional translation. In: Proceedings of OCEANS 1993, vol 8446. pp 277–282

  16. Eng Y, Lau W, Low E, Seet G, Chin C (2008) Identification of the hydrodynamics coefficients of an underwater vehicle using free decay pendulum motion. Eng Lett 16(3):326–331

    Google Scholar 

  17. Ross A, Fossen TI, Johansen TA (2004) Identification of underwater vehicle hydrodynamic coefficients using free decay tests. IFAC Proc 37(10):363–368

  18. Chan WL, Kang T, Lee YJ, Sung SK, Yoon KJ (2007) Swimming study on an ostraciiform fish robot. In: Proceedings international conference on control, automation and systems (ICCAS 2007). pp 700–705

  19. Bilo D, Nachtigall W (1980) A simple method to determine drag coefficients in aquatic animals. J Exp Biol 87:259–357

    Google Scholar 

  20. Chan WL, Kang T (2011) Simultaneous determination of drag coefficient and added mass. IEEE J Ocean Eng 36(3):422–430

    Article  Google Scholar 

  21. Caccia M, Indiveri G, Veruggio G (2000) Modeling and identification of open-frame variable configuration unmanned underwater vehicles. IEEE J Ocean Eng 25(2):227–240

    Article  Google Scholar 

  22. Smallwood DA, Whitcomb LL (2003) Adaptive identification of dynamically positioned underwater robotic vehicles. IEEE Trans Control Syst Technol 11(4):505–515

    Article  Google Scholar 

  23. Middletone RH, Goodwin GC (1986) Adaptive computed torque control for rigid link manipulators. In: Proceedings of 25th IEEE conference on decision and control. pp. 68–73

  24. Hsu P, Bodson M, Sastry S, Paden B (1987) Adaptive identification and control for manipulators without using joint accelerations. In: Proceedings of 1987 IEEE international conference on robotics and automation, vol 4. pp 1210–1215

  25. Slotine JJE, Li W (1989) Composite adaptive control of robot manipulators. Automatica 25(4):509–519

    Article  MathSciNet  MATH  Google Scholar 

  26. Fossen TI (1994) Guidance and control of ocean vehicles. Wiley, Chichester

    Google Scholar 

  27. Antonelli G (2014) Underwater robots, 3rd edn. Springer, New York

    Book  Google Scholar 

Download references

Acknowledgements

We thank the Naval Research Board (NRB), DRDO, India, for funding the work and the National Institute of Ocean Technology, Chennai, India, for providing the facilities to test the system. The first author would like to thank the Department of Science and Technology [DST], India, for supporting the research through the INSPIRE fellowship.

Author information

Authors and Affiliations

  1. Robotics Laboratory, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, 600036, India

    Thiyagarajan Ranganathan, Vijendra Singh & Asokan Thondiyath

Authors
  1. Thiyagarajan Ranganathan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vijendra Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Asokan Thondiyath
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Asokan Thondiyath.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ranganathan, T., Singh, V. & Thondiyath, A. Estimation of hydrodynamic parameters for underwater systems using a simple off-line regression method: a case study. J Mar Sci Technol 24, 968–983 (2019). https://doi.org/10.1007/s00773-018-0599-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00773-018-0599-2

Keywords

Search

Navigation