Skip to main content
SpringerLink
Log in

The ANFIS approach applied to AUV autopilot design

  • Articles
  • Published:
Neural Computing & Applications Aims and scope Submit manuscript

Abstract

This paper describes the application of neurofuzzy techniques in the design of autopilots for controlling the yaw dynamics of an autonomous underwater vehicle. Autopilots are designed using an Adaptive-Network-based Fuzzy Inference System (ANFIS), a chemotaxis tuning methodology and a fixed fuzzy rule-based approach. To describe the yaw dynamic characteristics of an autonomous underwater vehicle, a realistic simulation model is employed. Results are presented which demonstrate the superiority of the ANFIS approach. It is concluded that the approach offers a viable alternative method for designing such autopilots.

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

Similar content being viewed by others

References

  1. Cowling D. Full range autopilot design for an unmanned underwater vehicle. Proceedings of 1996 IFAC 13th Triennial World Congress, San Francisco, CA, 30 June–5 July 1996; Q: 339–344

    Google Scholar 

  2. Cristi R, Papoulias FA, Healey AJ. Adaptive sliding mode control of autonomous underwater vehicles in the dive plane. IEEE J Oceanic Eng 1990; 15(3): 152–160

    Google Scholar 

  3. Forsen TI, Fjellstad O-E. Robust adaptive control of underwater vehicles. Proceedings 3rd IFAC Workshop on Control Applications in Marine Systems, Trondheim, Norway, 10–12 May 1995; 66–74

  4. Farbrother HNR, Stacey BA, Sutton R. A self-organising controller for an ROV. Proceedings IEE Control 91, Edinburgh, UK, 1991; 499–504

  5. Smith SM, Rae GJS, Anderson DT, Shien AM. Fuzzy logic control of an autonomous underwater vehicle. Proceedings 1st IF AC International Workshop on Intelligent Autonomous Vehicles, Southampton, UK, 18–21 April 1993; 318–323

  6. Fujii T, Ura T. Development of motion control system for AUV using neural nets. Proceedings of AUV 90, Washington, 1990; 81–86

  7. Yuh J. A neural network controller for underwater robotic vehicles. IEEE J Oceanic Engineering 1990; 15(3): 161–166

    Google Scholar 

  8. Jang JSR. ANFIS: adaptive network-based fuzzy inference system. IEEE Trans Systems, Man Cybern 1993; 23(3): 665–685

    Google Scholar 

  9. Koshland DE. Bacterial chemotaxis in relation to neurobiology. Ann Rev Neurosci 1980; 3: 45–75

    Google Scholar 

  10. Marshfield WB. Submarine data set for use in autopilot research. Technical Memorandum, DRA/MAR TM (MTH) 92314, DRA Haslar, April 1992

  11. Sugeno M (ed). Industrial Applications of Fuzzy Control. North Holland, The Netherlands, 1985

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Marine Technology Division, Institute of Marine Studies, University of Plymouth, Drake Circus, PL4 8AA, Plymouth, UK

    R. Sutton & P. J. Craven

Authors
  1. R. Sutton
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. J. Craven
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sutton, R., Craven, P.J. The ANFIS approach applied to AUV autopilot design. Neural Comput & Applic 7, 131–140 (1998). https://doi.org/10.1007/BF01414165

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01414165

Keywords

Search

Navigation