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UID:calendar.253821.field_event_date.0@www.wright.edu
DTSTAMP:20260219T203804Z
CREATED:20171130T184734Z
DESCRIPTION:Ph.D. Committee:  Drs. John Gallagher\, Advisor\, Michael Rayme
 r\, Joseph Slater (ME)\, and Mateen RizkiABSTRACT:Cyber-Physical Systems (
 CPS) systems characterized by closely coupled physical and software compon
 ents that operate simultaneously on different spatial and temporal scales\
 ; exhibit multiple and distinct behavioral modalities\; and interact with 
 one another in ways not entirely predictable at the time of design.  A com
 monly appearing type of CPS contains one or more 'smart components' that a
 dapt locally in response to global measurements of whole system performanc
 e.   An example of a smart component robotic CPS system is a Flapping Wing
  Micro Air Vehicle (FW-MAV) that contains wing motion oscillators that con
 trol their wing flapping patterns to enable the whole system to fly precis
 ely after the wings are damaged in unpredictable ways. Localized learning 
 of wing flapping patterns using meta-heuristic search optimizing flight pr
 ecision has been shown effective in recovering flight precision after wing
  damage.  However\, such methods provide no insight into the nature of the
  damage that necessitated the learning.  Additionally\, if the learning is
  done while the FW-MAV is in service\, it is possible for the search algor
 ithm to actually damage the wings even more due to overly aggressive testi
 ng of candidate solutions.  In previous work\, a method was developed to e
 xtract estimates of wing damage as a side effect of the corrective learnin
 g of wing motion patterns.  Although effective\, that method lacked in two
  important respects.  First\, it did not settle on wing gait solutions qui
 ckly enough for the damage estimates to be created in a time acceptable to
  a user.  Second\, there were no protections against testing too aggressiv
 e wing motions that could potentially damage the system even further durin
 g the attempted behavior level repair. This work addresses both of those i
 ssues by making modifications to the representation and search space of wi
 ng motion patterns potentially visited by the online metaheuristic search.
   The overarching goals were to lessen the time required to achieve effect
 ive repair and damage estimates and to avoid further damage to wings by li
 miting the search's access to overly aggressive wing motions.  The key cha
 llenge was coming to understand how to modify representations and search s
 pace to provide the desired benefits without destroying the method's abili
 ty to find solutions at all.  With the recent emergence of functional inse
 ct-sized and bird-sized FW-MAV and an expected need to modify wing behavio
 r in service\, these believed to be the first of their kind studies are pa
 rticularly relevant.Selected PublicationsSam\, M.\, Boddhu\, S.\, Gallaghe
 r\, J.C. (2017).  A dynamic search space approach to improving learning in
  a simulated flapping wing micro air vehicle\,  in the Proceedings of the 
 2017 IEEE Congress on Evolutionary Computation\, IEEE Press\,  San Sebasti
 an\, Spain. Sam\, M.\, Boddhu\, S.\, Duncan\, K.\, Botha\, H.\, Gallagher\
 , J.C. (2016). Improving In-Flight Learning in a Flapping Wing Micro Air V
 ehicle\, International Journal of Monitoring and Surveillance Technologies
  Research (IJMSTR). Vol. 4.\, No. 1. Gallagher\, J.C.\, Sam\, M.\, Boddhu\
 , S.\, Matson\, E.\, Greenwood\, G. (2016).  Drag force fault extension to
  evolutionary model consistency checking for a flapping-wing micro air veh
 icle.  in the 2016 IEEE Congress on Evolutionary Computation Sam\, M.\, Bo
 ddhu\, S.\, Duncan\, K.\, Gallagher\, J.C. (2014). Evolutionary Strategy A
 pproach for Improved In-Flight Control Learning in a Simulated Insect-Scal
 e Flapping-Wing Micro Air Vehicle. in the 2014 IEEE International Conferen
 ce on Evolvable Systems (ICES) (ICES2014)Duncan\, K.\, Boddhu\, S.\, Sam\,
  M.\, Gallagher\, J.C. (2014). Islands of Fitness Compact Genetic  Algorit
 hm for Rapid In-Flight Control Learning in a Flapping-Wing Micro Air Vehic
 le:  A Search Space Reduction Approach. in the 2014 IEEE International Con
 ference on Evolvable Systems (ICES) (ICES2014)
DTSTART;TZID=America/New_York:20171206T090000
DTEND;TZID=America/New_York:20171206T110000
LAST-MODIFIED:20171130T184820Z
LOCATION:304 Russ Engineering
SUMMARY:Ph.D. Dissertation Defense “Adapting the Search Space while Limitin
 g Damage during Learning in a Simulated Flapping Wing Micro Air Vehicle” B
 y Monica Sam
URL;TYPE=URI:/events/phd-dissertation-defense-%E2%80%
 9Cadapting-search-space-while-limiting-damage-during-learning
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