International Journal of Dynamics and Control

, Volume 5, Issue 4, pp 1122–1138 | Cite as

Uncertainty and disturbance estimator based sliding mode control of an autonomous underwater vehicle

  • P. S. Londhe
  • Dinesh D. Dhadekar
  • B. M. PatreEmail author
  • L. M. Waghmare


In this paper, a highly non-linear model of an autonomous underwater vehicle (AUV) with six degrees-of-freedom is linearized to yaw (horizontal) and pitch (vertical) planes under several working conditions. For controlling steering and diving planes, an uncertainty disturbance estimator based sliding mode control (UDE-SMC) scheme is proposed and designed separately as single-input single-output controllers for horizontal and vertical plane dynamics of an AUV system. The proposed UDE-SMC scheme is effective in compensating the uncertainties in the hydrodynamic parameters of the vehicle and rejecting unpredictable disturbances due to ocean currents. The UDE-SMC consists of an equivalent and estimated lumped uncertain terms to suppress the effect of external disturbances and parametric uncertainties acting on the vehicle dynamics. Numerical simulations were performed to validate the UDE-SMC.


Autonomous underwater vehicle (AUV) Uncertainty and disturbance estimator Sliding mode control Steering plane Diving plane 


  1. 1.
    Yuh J (2000) Design and control of autonomous underwater robots: a survey. Auton Robots 8(1):7–24CrossRefGoogle Scholar
  2. 2.
    Healey A, Lienard D (1993) Multivariable sliding mode control for autonomous diving and steering of unmanned underwater vehicles. IEEE J Ocean Eng 18(3):327–339. doi: 10.1109/JOE.1993.236372 CrossRefGoogle Scholar
  3. 3.
    Silvestre C, Pascoal A (1997) Control of an auv in the vertical and horizontal planes: system design and tests at sea. Trans Inst Meas Control 19(3):126–138CrossRefGoogle Scholar
  4. 4.
    Healy A, Marco D (1992) Slow speed flight control of autonomous underwater vehicles: experimental results with NPS AUV II. In: Proceedings of the second international offshore and polar engineering conference, San Francisco, pp 523–532Google Scholar
  5. 5.
    Liu X, Liu Z, Shan J, Sun H (2015) Anti-disturbance autopilot design for missile system via finite time integral sliding mode control method and nonlinear disturbance observer technique. Trans Inst Meas Control. doi: 10.1177/0142331215603793 Google Scholar
  6. 6.
    Rodrigues L, Tavares P, Prado M (1996) Sliding mode control of an AUV in the diving and steering planes. In: OCEANS ’96. MTS/IEEE. Prospects for the 21st century. Conference proceedings, vol 2, pp 576–583. doi: 10.1109/OCEANS.1996.568291
  7. 7.
    Trebi-Ollennu A, White B (1997) A robust nonlinear control design for remotely operated vehicle depth control systems. Trans Inst Meas Control 19(3):119–125CrossRefzbMATHGoogle Scholar
  8. 8.
    Zhang S, Yu J, Zhang A (2010) Discrete-time quasi-sliding mode control of underwater vehicles. In: 2010 8th world congress on intelligent control and automation (WCICA), pp 6686–6690. doi: 10.1109/WCICA.2010.5554152
  9. 9.
    Narimani M, Loueipour M (2008) Robust control of autonomous underwater vehicles (AUVS). In: CCECE 2008. Canadian conference on electrical and computer engineering, pp 000,207–000,210. doi: 10.1109/CCECE.2008.4564525
  10. 10.
    Santhakumar M, Kim J (2011) Modelling, simulation and model reference adaptive control of autonomous underwater vehicle-manipulator systems. In: 2011 11th international conference on control, automation and systems (ICCAS), pp 643–648Google Scholar
  11. 11.
    Zhang X, Wei Y, Han Y, Bai T, Ma K (2015) Design and comparison of LQR and a novel robust backstepping controller for supercavitating vehicles. Trans Inst Meas Control. doi: 10.1177/0142331215607614 Google Scholar
  12. 12.
    Nag A, Patel S, Akbar S (2013) Fuzzy logic based depth control of an autonomous underwater vehicle. In: 2013 International multi-conference on automation, computing, communication, control and compressed sensing (iMac4s), pp 117–123. doi: 10.1109/iMac4s.2013.6526393
  13. 13.
    Smith S, Rae GJS, Anderson DT (1993) Applications of fuzzy logic to the control of an autonomous underwater vehicle. In: Second IEEE international conference on fuzzy systems, vol 2, pp 1099–1106. doi: 10.1109/FUZZY.1993.327361
  14. 14.
    Li JH, Lee PM, Lee SJ (2002) Motion control of an auv using a neural network adaptive controller. In: Proceedings of the 2002 international symposium on underwater technology, pp 217–221. doi: 10.1109/UT.2002.1002429
  15. 15.
    Li JH, Lee PM, Lee SJ (2002) Neural net based nonlinear adaptive control for autonomous underwater vehicles. In: Proceedings of ICRA ’02. IEEE international conference on robotics and automation, vol 2, pp 1075–1080. doi: 10.1109/ROBOT.2002.1014686
  16. 16.
    Lisboa P, Kodogiannis V, Lucas J (1997) Neural network identification and control of an underwater vehicle. Trans Inst Meas Control 19(4):202–210CrossRefGoogle Scholar
  17. 17.
    van de Ven PW, Flanagan C, Toal D (2005) Neural network control of underwater vehicles. Eng Appl Artif Intell 18(5):533–547CrossRefGoogle Scholar
  18. 18.
    Youcef-Toumi K, Ito O (1990) A time delay controller for systems with unknown dynamics. J Dyn Syst Meas Control 112(1):133–142CrossRefzbMATHGoogle Scholar
  19. 19.
    Chen WH (2003) Nonlinear disturbance observer-enhanced dynamic inversion control of missiles. J Guid Control Dyn 26(1):161–166CrossRefGoogle Scholar
  20. 20.
    Ginoya D, Shendge PD, Patre BM, Phadke SB (2014) A new state and perturbation observer based sliding mode controller for uncertain systems. Int J Dyn Control 4(1):92–103CrossRefMathSciNetGoogle Scholar
  21. 21.
    Zhong QC, Rees D (2004) Control of uncertain LTI systems based on an uncertainty and disturbance estimator. J Dyn Syst Meas Control 126(4):905–910CrossRefGoogle Scholar
  22. 22.
    Deshpande V, Phadke S (2012) Control of uncertain nonlinear systems using an uncertainty and disturbance estimator. J Dyn Syst Meas Control 134(2):024501CrossRefGoogle Scholar
  23. 23.
    Kuperman A, Zhong QC (2011) Robust control of uncertain nonlinear systems with state delays based on an uncertainty and disturbance estimator. Int J Robust Nonlinear Control 21(1):79–92CrossRefzbMATHMathSciNetGoogle Scholar
  24. 24.
    Suryawanshi PV, Shendge PD, Phadke S (2014) Robust sliding mode control for a class of nonlinear systems using inertial delay control. Nonlinear Dyn 78(3):1921–1932CrossRefGoogle Scholar
  25. 25.
    Suryawanshi PV, Shendge PD, Phadke SB (2015) A boundary layer sliding mode control design for chatter reduction using uncertainty and disturbance estimator. Int J Dyn Control 1–10. doi: 10.1007/s40435-015-0150-9
  26. 26.
    Talole S, Phadke S (2008) Model following sliding mode control based on uncertainty and disturbance estimator. J Dyn Syst Meas Control 130(3):034501CrossRefGoogle Scholar
  27. 27.
    SNAME (1950) Nomenclature for treating the motion of a submerged body through a fluid. The society of naval architects and marine engineers, technical and reserach bulletin no. 1–5, pp 1–15Google Scholar
  28. 28.
    Fossen TI (1994) Guidance and control of ocean vehicles. Wiley, New YorkGoogle Scholar
  29. 29.
    Jalving B (1994) The NDRE-AUV flight control system. IEEE J Ocean Eng 19(4):497–501. doi: 10.1109/48.338385 CrossRefGoogle Scholar
  30. 30.
    Antonelli G (2014) Underwater robots motion and force control of vehicle-manipulator systems. Springer Tracts in Advanced Robotics, BerlinzbMATHGoogle Scholar
  31. 31.
    Londhe PS, Santhakumar M, Patre BM, Waghmare LM (2016) Task space control of an autonomous underwater vehicle manipulator system by robust single-input fuzzy logic control scheme. IEEE J Ocean Eng. doi: 10.1109/joe.2016.2548820 Google Scholar
  32. 32.
    Mohan S, Kim J (2015) Coordinated motion control in task space of an autonomous underwater vehiclemanipulator system. Ocean Eng 104:155–167CrossRefGoogle Scholar
  33. 33.
    Yatoh T, Sagara S, Tamura M (2008) Digital type disturbance compensation control of a floating underwater robot with 2 link manipulator. Artif Life Robot 13(1):377–381CrossRefGoogle Scholar
  34. 34.
    Venugopal KP, Sudhakar R, Pandya AS (1992) On-line learning control of autonomous underwater vehicles using feedforward neural networks. IEEE J Ocean Eng 17(4):308–319CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • P. S. Londhe
    • 1
  • Dinesh D. Dhadekar
    • 1
  • B. M. Patre
    • 1
    Email author
  • L. M. Waghmare
    • 1
  1. 1.Department of Instrumentation EngineeringShri Guru Gobind Singhji Institute of Engineering and TechnologyVishnupuri, NandedIndia

Personalised recommendations