Skip to main content

Advertisement

Springer Nature Link
Log in

Fuzzy Logic Based Approach to Design of Flight Control and Navigation Tasks for Autonomous Unmanned Aerial Vehicles

  • Published:
Journal of Intelligent and Robotic Systems Aims and scope Submit manuscript

Abstract

This paper proposes a fuzzy logic based autonomous navigation controller for UAVs (unmanned aerial vehicles). Three fuzzy logic modules are developed under the main navigation system for the control of the altitude, the speed, and the heading, through which the global position (latitude–longitude) of the air vehicle is controlled. A SID (Standard Instrument Departure) and TACAN (Tactical Air Navigation) approach is used and the performance of the fuzzy based controllers is evaluated with time based diagrams under MATLAB’s standard configuration and the Aerosim Aeronautical Simulation Block Set which provides a complete set of tools for rapid development of detailed six-degree-of-freedom nonlinear generic manned/unmanned aerial vehicle models. The Aerosonde UAV model is used in the simulations in order to demonstrate the performance and the potential of the controllers. Additionally, FlightGear Flight Simulator and GMS aircraft instruments are deployed in order to get visual outputs that aid the designer in the evaluation of the controllers. Despite the simple design procedure, the simulated test flights indicate the capability of the approach in achieving the desired performance.

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
$34.99 /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 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

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Doitsidis, L., Valavanis, K.P., Tsourveloudis, N.C., Kontitsis, M.: A framework for fuzzy logic based UAV navigation and control. In: Proceedings of the International Conference on Robotics Automation, vol. 4, pp. 4041–4046 (2004)

  2. Schumacher, C.J., Kumar, R.: Adaptive control of UAVs in close-coupled formation flight. In: Proceedings of the American Control Conference, vol. 2, pp. 849–853 (2000)

  3. Andrievsky, B., Fradkov, A.: Combined adaptive autopilot for an UAV flight control. In: Proceedings of the 2002 International Conference on Control Applications, vol. 1, pp. 290–291 (2002)

  4. Dufrene, W.R., Jr.: Application of artificial intelligence techniques in uninhabited aerial vehicle flight. In: The 22nd Digital Avionics Systems Conference vol. 2, pp. 8.C.3–8.1-6 (2003)

  5. Li, Y., Sundararajan, N., Sratchandran, P.: Neuro-controller design for nonlinear fighter aircraft maneuver using fully tuned RBF networks. Automatica 37, 1293–1301 (2001)

    Article  MATH  Google Scholar 

  6. Borrelli, F., Keviczky, T., Balas, G.J.: Collision-free UAV formation flight using decentralized optimization and invariant sets. In: 43rd IEEE Conference on Decision and Control vol. 1, pp. 1099–1104 (2004)

  7. Marin, J.A., Radtke, R., Innis, D., Barr, D.R., Schultz, A.C.: Using a genetic algorithm to develop rules to guide unmanned aerial vehicles. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, vol. 1, pp. 1055–1060. (1999)

  8. Ren, W., Beard, R.W.: CLF-based tracking control for UAV kinematic models with saturation constraints. In: Proceedings of the 42nd IEEE Conference on Decision and Control, vol. 4, pp. 3924–3929 (2003)

  9. Dathbun, D., Kragelund, S., Pongpunwattana, A., Capozzi, B.: An evolution based path planning algorithm for autonomous motion of a UAV through uncertain environments. In: Proceedings of the 21st Digital Avionics Systems Conference vol. 2, pp. 8D2-1–8D2-12 (2002)

  10. Global Robotic Observation System, Definition Of Aerosonde UAV Specifications: http://www.aerosonde.com/aircraft/

  11. Unmanned Dynamics, Aerosim Aeronautical Simulation Block Set Version 1.2 User’s Guide: http://www.u-dynamics.com/aerosim/default.htm

  12. FlightGear Open-source Flight Simulator. www.flightgear.org

  13. Qiau, W., Muzimoto, M.: PID type fuzzy controller and parameter adaptive method. Fuzzy Sets Syst. 78, 23–35 (1995)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Turkish Air Force Academy, ASTIN, Yesilyurt, Istanbul, 34807, Turkey

    Sefer Kurnaz & Omer Cetin

  2. Department of Electrical and Electronic Engineering, Bogazici University, Bebek, 80815, Istanbul, Turkey

    Okyay Kaynak

Authors
  1. Sefer Kurnaz
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Omer Cetin
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Okyay Kaynak
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Sefer Kurnaz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kurnaz, S., Cetin, O. & Kaynak, O. Fuzzy Logic Based Approach to Design of Flight Control and Navigation Tasks for Autonomous Unmanned Aerial Vehicles. J Intell Robot Syst 54, 229–244 (2009). https://doi.org/10.1007/s10846-008-9263-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-008-9263-0

Keywords