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dc.contributor.authorQu, Yan
dc.date.accessioned2014-03-04T20:22:16Z
dc.date.available2014-03-04T20:22:16Z
dc.date.issued2011-08
dc.identifier.otherqu_yan_201108_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/qu_yan_201108_ms
dc.identifier.urihttp://hdl.handle.net/10724/27567
dc.description.abstractAutonomous 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.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectUnmanned Aerial Vehicle
dc.subjectLearning Fuzzy Classifier System
dc.subjectFuzzy Control
dc.titleAn Unmanned Aerial Vehicle Controller based on a Learning Classifier System
dc.typeThesis
dc.description.degreeMS
dc.description.departmentArtificial Intelligence Center
dc.description.majorArtificial Intelligence
dc.description.advisorWalter D. Potter
dc.description.committeeWalter D. Potter
dc.description.committeeKhaled Rasheed
dc.description.committeeSuchendra M. Bhandarkar


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