An Unmanned Aerial Vehicle Controller Based on a Learning Fuzzy Classifier System
نویسندگان
چکیده
AN UNMANNED AERIAL VEHICLE CONTROLLER BASED ON A LEARNING FUZZY CLASSIFIER SYSTEM by YAN QU (Under the Direction of Walter D. Potter) ABSTRACT Autonomous Unmanned Aerial Vehicle (UAVs) have been increasingly employed by researchers, commercial organizations and the military to perform a variety of missions. This thesis discusses the design of an autonomous controller using a Learning Fuzzy Classifier System (LFCS) to store and evolve fuzzy rules and fuzzy membership functions. The controller executes the fuzzy inference process and assigns credit to the population during a flight simulation. This framework is useful in evolving a sophisticated set of rules for the controller of a UAV, which deals with uncertainty in both its internal state and external environment. A flight simulation is implemented in Matlab/Simulink providing the opportunity to assess the accuracy of the control rules. The simulation results show that this approach is able to develop a controller that achieves high effectiveness in both lateral and longitudinal control.
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