Autonomous Robots

, Volume 3, Issue 2–3, pp 187–194 | Cite as

Experimental study on a learning control system with bound estimation for underwater robots

  • S. K. Choi
  • J. Yuh


Underwater robotic vehicles (URVs) have become an important tool for numerous underwater tasks due to their greater speed, endurance, depth capability, and a higher factor of safety than human divers. However, most vehicle control system designs are based on simplified vehicle models and often result in poor vehicle performance due to the nonlinear and time-varying vehicle dynamics having parameter uncertainties. Conventional proportional-integral-derivative (PID) type controllers cannot provide good performance without fine-tuning the controller gains and may fail for sudden changes in the vehicle dynamics and its environment. Conventional adaptive control systems based on parameter adaptation techniques also fail in the presence of unmodeled dynamics.

This paper describes a new vehicle control system using the bound estimation techniques, capable of learning, and adapting to changes in the vehicle dynamics and parameters. The control system was extensively “wet-tested” on the Omni-Directional Intelligent Navigator (ODIN)-a six degree-of-freedom, experimental underwater vehicle developed at the Autonomous Systems Laboratory, and its performance was compared with the performance of a conventional linear control system. The results showed the controller's ability to provide good performance in the presence of unpredictable changes in the vehicle dynamics and its environment, and it's capabilities of learning and adapting.


learning control adaptive control underwater robot 


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  1. Adam, J.A. 1985. Probing beneath the sea. IEEE Spectrum, pp. 55–64.Google Scholar
  2. Astrom, K.J. and Wittenmark, B. 1989. Adaptive Control, Addison Wesley.Google Scholar
  3. Bellingham, J.G. and Chryssostomidis, C. 1993. Economic ocean survey capability with AUVs. Sea Technology, pp. 12–18.Google Scholar
  4. Blidberg, D.R. 1991. Autonomous underwater vehicles: A tool for the ocean, unmanned systems. Spring, pp. 10–15.Google Scholar
  5. Britton, P. 1995. Undersea explorers. Popular Science, pp. 39–42.Google Scholar
  6. Brock, D.L., Montana, D.J., and Ceranowicz, A.Z. 1992. Coordination and control of multiple autonomous vehicles. Proc. 1992 IEEE Conf. Robotics and Automation, pp. 2725–2730.Google Scholar
  7. Choi, S.K., Yuh, J., and Keevil, N. 1993. Design of omni-directional underwater robotic vehicle. Proc. IEEE Oceans. 1993.Google Scholar
  8. Choi, S.K., Yuh, J., and Takashige, G. 1995. Development of the omni-directional intelligent navigator. IEEE Robotics and Automation Magazine, pp. 44–53.Google Scholar
  9. Cristi, R., Papoulias, F.A., and Healey, A.J. 1991. Adaptive sliding mode control of autonomous underwater vehicles in the dive plane. IEEE J. of Oceanic Engineering, 15(3):462–470.Google Scholar
  10. Dane, A. 1993. Robots of the deep. Popular Mechanics, pp. 104–105.Google Scholar
  11. Dougherty, F. and Woolweaver, G. 1990. At sea testing of an under-water vehicle flight control system. Proc. AUV'90, pp. 51–59.Google Scholar
  12. Fossen, T.I. 1991. Nonlinear Modeling and Control of Underwater Vehicles. Dr. Ing Thesis, NIT.Google Scholar
  13. Fossen, T.I. 1995. Underwater vehicle dynamics. Underwater Robotic Vehicles: Design and Control, Yuh (ed.), pp. 15–40.Google Scholar
  14. Goheen, K.R. and Jefferys, E.R. 1990. Multivariable self-tuning autopilots for autonomous and remotely operated underwater vehicles. IEEE J. of Oceanic Engr., 15(3):144–151.Google Scholar
  15. Healey, A.J. and Marco, D.B. 1992. Slow speed flight control of autonomous underwater vehicles: Experimental results with NPS AUV II. Proc. ISOPE, pp. 523–532.Google Scholar
  16. Kato, N., Ito, Y., Kojima, J., Asakawa, K., and Shirasaki, Y. 1993. Guidance and control of autonomous underwater vehicle AQUA explorer 1000 for inspection of underwater cables. Proc. 8th Int'l. Sym. on Unmanned, Untethered Submersible Technology.Google Scholar
  17. Lewis, D.J., Lipscomb, J.M., and Thompson, P.G. 1984. The simulation of remotely operated underwater vehicles. ROV'84, pp. 245–252.Google Scholar
  18. Mahesh, H., Yuh, J., and Lakshmi, R. 1991. A coordinated control of an underwater vehicle and robotic manipulator. J. of Robotic Systems, 8(3):339–370.Google Scholar
  19. Nomoto, M. and Hattori, M. 1986. A deep ROV, dolphin 3K: Design and performance analysis. IEEE Journal of Oceanic Engineering, V/OE-11(3):373–391.Google Scholar
  20. Sagatun, S.I. 1992. Modeling and control of underwater vehicles: Lagrangian approach. Dr. Ing. Thesis, Norwegian Institute of Technology.Google Scholar
  21. Tucker, J.B. 1986. Submersibles reach new depths. High Technology, pp. 17–24.Google Scholar
  22. Yoerger, D.N. and Slotine, J.E. 1985. Robust trajectory control of underwater vehicles. IEEE J. of Oceanic Engineering, OE-10(4):462–470.Google Scholar
  23. Yuh, J. 1990a. A neural net controller for underwater robotic vehicles. IEEE J. Oceanic Engineering, 15(3):161–166.Google Scholar
  24. Yuh, J. 1990b. Modeling and control of underwater robotic vehicles. IEEE Trans. Sys., Man and Cyber., 20(6).Google Scholar
  25. Yuh, J. 1994. Learning control for underwater robotic vehicles. IEEE Control Systems Magazine, pp. 39–46.Google Scholar
  26. Yuh, J. (ed.) 1995. Underwater Robotic Vehicles: Design and Control, TSI Press.Google Scholar
  27. Yuh, J. 1995. Development of underwater robotics. 1995 IEEE Conference on Robotics and Automation, Nagoya, Japan.Google Scholar

Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • S. K. Choi
    • 1
  • J. Yuh
    • 1
  1. 1.Department of Mechanical Engineering, University of HawaiiAutonomous Systems LaboratoryHonoluluUSA

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