Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-controller design for nonlinear fighter aircraft maneuver using fully tuned RBF networks
Automatica (Journal of IFAC)
Survey paper: A survey on industrial applications of fuzzy control
Computers in Industry
Autonomous quadrotor flight with vision-based obstacle avoidance in virtual environment
Expert Systems with Applications: An International Journal
Review: Hybrid expert systems: A survey of current approaches and applications
Expert Systems with Applications: An International Journal
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
Adaptive neuro fuzzy controller for adaptive compliant robotic gripper
Expert Systems with Applications: An International Journal
Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces
Expert Systems with Applications: An International Journal
Intelligent rotational direction control of passive robotic joint with embedded sensors
Expert Systems with Applications: An International Journal
Review Article: Applications of neuro fuzzy systems: A brief review and future outline
Applied Soft Computing
Journal of Intelligent and Robotic Systems
Hi-index | 12.06 |
In this paper, an ANFIS (adaptive neuro-fuzzy inference system) based autonomous flight controller for UAVs (unmanned aerial vehicles) is described. To control the position of the UAV in three dimensional space as altitude and longitude-latitude location, three fuzzy logic modules are developed. These adjust the pitch angle, the roll angle and the throttle position of the UAV so that its altitude, the heading and the speed are controlled together. 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 six degree-of-freedom nonlinear generic manned/unmanned aerial vehicle models. To demonstrate the performance and potential of the controllers, the Aerosonde UAV model is used. Flight Gear open source flight simulator and Gauges Block Set are deployed in order to get visual outputs that aid the designer in the evaluation of the controllers. Steep turn maneuvers which are used for basic training of pilots are applied to test the performance of the fuzzy logic controllers. Despite the simple design procedure, the simulated test flights indicate the capability of the approach in achieving the desired performance.