Publication Date
2019
Document Type
Thesis
Committee Members
John C. Gallagher, Ph.D. (Advisor); Michael L. Raymer, Ph.D. (Committee Member); Mateen Rizki, Ph.D. (Committee Member)
Degree Name
Master of Science in Computer Engineering (MSCE)
Abstract
On-going effective control of insect-scale Flapping-Wing Micro Air Vehicles could be significantly advantaged by active in-flight control adaptation. Previous work demonstrated that in simulated vehicles with wing membrane damage, in-flight recovery of effective vehicle attitude and vehicle position control precision via use of an in-flight adaptive learning oscillator was possible. Most recent approaches to this problem employ an island-of-fitness compact genetic algorithm (ICGA) for oscillator learning. The work presented provides the details of a domain specific search space reduction approach implemented with existing ICGA and its effect on the in-flight learning time. Further, it will be demonstrated that the proposed search space reduction methodology is effective in producing an error correcting oscillator configuration rapidly, online, while the vehicle is in normal service.
Page Count
55
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2019
Copyright
Copyright 2019, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
