A Frugal Fuzzy Logic Based Approach for Autonomous Flight Control of Unmanned Aerial Vehicles

This paper proposes a fuzzy logic based autonomous flight controller for UAVs (unmanned aerial vehicles). Three fuzzy logic modules are developed for the control of the altitude, the speed, and the roll angle, through which the altitude and the latitude-longitude of the air vehicle are controlled. The implementation framework utilizes MATLAB's standard configuration and the Aerosim Aeronautical Simulation Block Set which provides a complete set of tools for rapid development of detailed 6 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, Microsoft Flight Simulator and FlightGear Flight Simulator 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.

[1]  Jr. Dufrene W.R. Application of artificial intelligence techniques in uninhabited aerial vehicle flight , 2003 .

[2]  Rajeeva Kumar,et al.  Adaptive control of UAVs in close-coupled formation flight , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[3]  Kimon P. Valavanis,et al.  A framework for fuzzy logic based UAV navigation and control , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[4]  F. Borrelli,et al.  Collision-free UAV formation flight using decentralized optimization and invariant sets , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[5]  Narasimhan Sundararajan,et al.  Neuro-controller design for nonlinear fighter aircraft maneuver using fully tuned RBF networks , 2001, Autom..

[6]  D. Rathbun,et al.  An evolution based path planning algorithm for autonomous motion of a UAV through uncertain environments , 2002, Proceedings. The 21st Digital Avionics Systems Conference.

[7]  Alexander L. Fradkov,et al.  Combined adaptive autopilot for an UAV flight control , 2002, Proceedings of the International Conference on Control Applications.

[8]  J.A. Marin,et al.  Using a genetic algorithm to develop rules to guide unmanned aerial vehicles , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[9]  Masaharu Mizumoto,et al.  PID type fuzzy controller and parameters adaptive method , 1996, Fuzzy Sets Syst..

[10]  Randal W. Beard,et al.  CLF-based tracking control for UAV kinematic models with saturation constraints , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).