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DTSTART:20191103T020000
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UID:calendar.379136.field_event_date.0@www.wright.edu
DTSTAMP:20260220T000911Z
CREATED:20191206T215330Z
DESCRIPTION:Committee:  Drs. John Gallagher\, Advisor\, Mateen Rizki\, and 
 Michael RaymerABSTRACT:On-going effective control of insect-scale Flapping
 -Wing Micro Air Vehicles could be significantly advantaged by active in-fl
 ight control adaptation. Previous work demonstrated that in simulated vehi
 cles with wing membrane damage\, in-flight recovery of effective vehicle a
 ttitude and vehicle position control precision via use of an in-flight ada
 ptive learning oscillator was possible. Most recent approaches to this pro
 blem employ an island-of-fitness compact genetic algorithm (ICGA) for osci
 llator learning. The work presented provides the details of a domain speci
 fic search space reduction approach implemented with existing ICGA and its
  effect on the in-flight learning time. Further\, it will be demonstrated 
 that theproposed search space reduction methodology is effective in produc
 ing an error correcting oscillator configuration rapidly\, online\, while 
 the vehicle is in normal service.
DTSTART;TZID=America/New_York:20191211T100000
DTEND;TZID=America/New_York:20191211T120000
LAST-MODIFIED:20191209T133126Z
LOCATION:304 Russ Engineering
SUMMARY:Masters Thesis Defense “Islands of Fitness Compact Genetic Algorith
 m for Rapid In-Flight Control Learning in a Flapping-Wing Micro Air Vehicl
 e: A Search Space Reduction Approach” By Kayleigh Duncan
URL;TYPE=URI:/events/masters-thesis-defense-%E2%80%9C
 islands-fitness-compact-genetic-algorithm-rapid-flight-control
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