SpringerLink
Log in

A Voltage Control Method of 6-DoF Underwater Robotic System with an Observer-Based Robust Adaptive Fuzzy Estimator

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

In this paper, a control method is presented for the six degrees of freedom (6-DoF) underwater systems. First, the proposed thruster voltage map** control strategy of underwaters is proposed by combining the general model of the 6-DoF motion and the thruster dynamical model. Indeed, a novel control strategy is designed to be used for the 6-DoF underwater vehicle with various numbers of thrusters. The suggested technique is computationally simple by using only one control loop, and it overcomes problems arising from conventional methods. Second, an observer-based robust adaptive fuzzy estimator is presented to compensate for disturbance and uncertainties.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

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.

Similar content being viewed by others

References

  1. Fernandez RAS, Milosevic Z, Dominguez S et al (2019) Nonlinear attitude control of a spherical underwater vehicle. Sensors 19:1445

    Article  Google Scholar 

  2. Ravell DAM, Maia MM, Diez FJ (2018) Modeling and control of unmanned aerial/underwater vehicles using hybrid control. Control Eng Pract 76:112–122

    Article  Google Scholar 

  3. Shojaei K (2019) Three-dimensional neural network tracking control of a moving target by underactuated autonomous underwater vehicles. Neural Comput Appl 31:509–521

    Article  Google Scholar 

  4. Fernandez RAS, Grande D, Martins A et al (2019) Modeling and control of underwater mine explorer robot UX-1. IEEE Access 7:39432–39447

    Article  Google Scholar 

  5. Liu H, Lyu Y, Lewis FL, Wan Y (2019) Robust time-varying formation control for multiple underwater vehicles subject to nonlinearities and uncertainties. Int J Robust Nonlinear Control 29:2712–2724

    Article  MathSciNet  MATH  Google Scholar 

  6. Heshmati-Alamdari S, Bechlioulis CP, Karras GC et al (2018) A robust interaction control approach for underwater vehicle manipulator systems. Annu Rev Control 46:315–325

    Article  MathSciNet  Google Scholar 

  7. Su Z, Zhou M, Han F et al (2019) Attitude control of underwater glider combined reinforcement learning with active disturbance rejection control. J Mar Sci Technol 24:686–704

    Article  Google Scholar 

  8. Silvestre C, Pascoal A (2007) Depth control of the INFANTE AUV using gain-scheduled reduced order output feedback. Control Eng Pract 15:883–895

    Article  Google Scholar 

  9. Bessa WM, Dutra MS, Kreuzer E (2008) Depth control of remotely operated underwater vehicles using an adaptive fuzzy sliding mode controller. Robot Auton Syst 56:670–677. https://doi.org/10.1016/j.robot.2007.11.004

    Article  Google Scholar 

  10. Bessa WM, Dutra MS, Kreuzer E (2010) An adaptive fuzzy sliding mode controller for remotely operated underwater vehicles. Rob Auton Syst 58:16–26

    Article  Google Scholar 

  11. Dong M, Li J, Chou W (2019) Depth control of ROV in nuclear power plant based on fuzzy PID and dynamics compensation. Microsyst Technol 26:811–821

    Article  Google Scholar 

  12. Batmani Y, Najafi S (2019) Event-triggered H∞ depth control of remotely operated underwater vehicles. IEEE Trans Syst Man, Cybern Syst 51:1224–1232

    Article  Google Scholar 

  13. Zhou H, Liu K, Xu H, Feng X (2019) Experimentally verified depth control of an unmanned semi-submersible vehicle. IEEE Access 7:94254–94262

    Article  Google Scholar 

  14. Tanakitkorn K, Wilson PA, Turnock SR, Phillips AB (2017) Depth control for an over-actuated, hover-capable autonomous underwater vehicle with experimental verification. Mechatronics 41:67–81

    Article  Google Scholar 

  15. Chatchanayuenyong T, Parnichkun M (2006) Neural network based-time optimal sliding mode control for an autonomous underwater robot. Mechatronics 16:471–478

    Article  Google Scholar 

  16. Ghavidel HF, Kalat AA (2017) Robust control for MIMO hybrid dynamical system of underwater vehicles by composite adaptive fuzzy estimation of uncertainties. Nonlinear Dyn 89:2347–2365. https://doi.org/10.1007/s11071-017-3590-2

    Article  MathSciNet  Google Scholar 

  17. Ghavidel HF, Kalat AA (2018) Observer-based hybrid adaptive fuzzy control for affine and nonaffine uncertain nonlinear systems. Neural Comput Appl 30:1187–1202. https://doi.org/10.1007/s00521-016-2732-7

    Article  Google Scholar 

  18. Ghavidel HF, Kalat AA (2017) Observer-based robust composite adaptive fuzzy control by uncertainty estimation for a class of nonlinear systems. Neurocomputing 230:100–109. https://doi.org/10.1016/j.neucom.2016.12.001

    Article  Google Scholar 

  19. Ghavidel HF (2020) A modeling error-based adaptive fuzzy observer approach with input saturation analysis for robust control of affine and non-affine systems. Soft Comput 24:1717–1735. https://doi.org/10.1007/s00500-019-03999-0

    Article  MATH  Google Scholar 

  20. Ghavidel HF (2018) Robust control of large scale nonlinear systems by a hybrid adaptive fuzzy observer design with input saturation. Soft Comput 22:6473–6487. https://doi.org/10.1007/s00500-017-2699-z

    Article  MATH  Google Scholar 

  21. Ghavidel HF, Kalat AA, Ghorbani V (2017) Observer-based robust adaptive fuzzy approach for current control of robot manipulators by estimation of uncertainties. Modares Mech Eng 17:286–294

    Google Scholar 

  22. Chang X-H, Qiao M-Y, Zhao X (2021) Fuzzy energy-to-peak filtering for continuous-time nonlinear singular system. IEEE Trans Fuzzy Syst 30:2325–2336

    Article  Google Scholar 

  23. Chang X-H, ** X (2022) Observer-based fuzzy feedback control for nonlinear systems subject to transmission signal quantization. Appl Math Comput 414:126657

    MathSciNet  MATH  Google Scholar 

  24. Shen H, Hu X, Wang J, et al (2021) Non-fragile H∞ synchronization for Markov jump singularly perturbed coupled neural networks subject to double-layer switching regulation. IEEE Trans Neural Networks Learn Syst

  25. Zhang Z, Chen Z, Sheng Z et al (2022) Static output feedback secure synchronization control for Markov jump neural networks under hybrid cyber-attacks. Appl Math Comput 430:127274

    MathSciNet  MATH  Google Scholar 

  26. Ghavidel HF, Mousavi-G SM (2022) Modeling analysis, control, and type-2 fuzzy energy management strategy of hybrid fuel cell-battery-supercapacitor systems. J Energy Storage 51:104456

    Article  Google Scholar 

  27. Ghavidel HF, Mousavi-G SM (2022) Observer-based type-2 fuzzy approach for robust control and energy management strategy of hybrid energy storage systems. Int J Hydrogen Energy 47:14983–15000

    Article  Google Scholar 

  28. Ghavidel HF, Kalat AA (2019) Synchronization adaptive fuzzy gain scheduling PID controller for a class of mimo nonlinear systems. Int J Uncertain Fuzziness Knowl-Based Syst 27:515–535

    Article  MathSciNet  MATH  Google Scholar 

  29. Ghavidel HF, Kalat AA (2017) Robust composite adaptive fuzzy identification control of uncertain MIMO nonlinear systems in the presence of input saturation. Arab J Sci Eng 42:5045–5058. https://doi.org/10.1007/s13369-017-2552-9

    Article  MathSciNet  MATH  Google Scholar 

  30. Rigatos GG (2009) Adaptive fuzzy control of DC motors using state and output feedback. Electr Power Syst Res 79:1579–1592

    Article  Google Scholar 

  31. Wang L-X (1996) Stable adaptive fuzzy controllers with application to inverted pendulum tracking. IEEE Trans Syst Man Cybern Part B 26:677–691

    Article  Google Scholar 

  32. Poursamad A, Davaie-Markazi AH (2009) Robust adaptive fuzzy control of unknown chaotic systems. Appl Soft Comput 9:970–976

    Article  Google Scholar 

  33. Park J-H, Seo S-J, Park G-T (2003) Robust adaptive fuzzy controller for nonlinear system using estimation of bounds for approximation errors. Fuzzy Sets Syst 133:19–36

    Article  MathSciNet  MATH  Google Scholar 

  34. Ho HF, Wong Y-K, Rad AB (2009) Adaptive fuzzy sliding mode control with chattering elimination for nonlinear SISO systems. Simul Model Pract Theory 17:1199–1210

    Article  Google Scholar 

  35. Chin C, Lau M, Low E, Seet G. Dynamic modelling and cascaded controller design of a low-speed maneuvering ROV. Adv Technol Res Dev Appl B 159–186

  36. Mian AA, Daobo W (2008) Modeling and backstep**-based nonlinear control strategy for a 6 DOF quadrotor helicopter. Chinese J Aeronaut 21:261–268

    Article  Google Scholar 

  37. Whitcomb LL, Yoerger DR (1999) Development, comparison, and preliminary experimental validation of nonlinear dynamic thruster models. IEEE J Ocean Eng 24:481–494

    Article  Google Scholar 

  38. Smallwood DA, Whitcomb LL (2004) Model-based dynamic positioning of underwater robotic vehicles: theory and experiment. IEEE J Ocean Eng 29:169–186

    Article  Google Scholar 

  39. Chin CS, Lum SH (2011) Rapid modeling and control systems prototy** of a marine robotic vehicle with model uncertainties using xPC target system. Ocean Eng 38:2128–2141

    Article  Google Scholar 

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

    Article  Google Scholar 

  41. Fossen TI et al (1994) Guidance and control of ocean vehicles. Wiley, New York

    Google Scholar 

  42. Ghavidel HF, Mousavi-G SM, Asad R (2020) Thrust control of BLDC thruster motors by observer-based robust adaptive fuzzy control. J Iran Assoc Electr Electron Eng 17:109–118

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Iran University of Science and Technology (IUST), Tehran, Iran

    Hesam Fallah Ghavidel, S. Mohammad Mousavi-G & Mohammad Ali Sandidzadeh

Authors
  1. Hesam Fallah Ghavidel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Mohammad Mousavi-G
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Ali Sandidzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hesam Fallah Ghavidel.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Details of parameters used for the thruster dynamics are listed in Table 2.

Table 2 The thruster design parameters
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fallah Ghavidel, H., Mousavi-G, S.M. & Sandidzadeh, M.A. A Voltage Control Method of 6-DoF Underwater Robotic System with an Observer-Based Robust Adaptive Fuzzy Estimator. Neural Process Lett 55, 6611–6635 (2023). https://doi.org/10.1007/s11063-023-11151-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-023-11151-1

Keywords

Search

Navigation