An iterative learning approach for online flight path optimization for tethered energy systems undergoing cyclic spooling motion

Mitchell Cobb, Kira Barton, Hosam Fathy, Chris Vermillion

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

This paper presents an iterative learning based approach for optimizing the crosswind flight path of an energy-harvesting tethered system that executes cyclic spool-inlspool-out motions. Through the combination of a high-tension crosswind spool-out motion (made possible through a high lift wing) and low-tension spool-in motion, net energy is generated at every cycle. Because the net energy generated by the system is highly sensitive to the crosswind flight patterns used on spool-out, and because the motions of the system are repetitive, we use an iterative learning formulation to optimize the flight patterns in real time. Using a medium-fidelity dynamic model, we demonstrate that an optimization approach based on iterative learning control (ILC) significantly increases the average power generated by such a system.

Original languageEnglish (US)
Title of host publication2019 American Control Conference, ACC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2164-2170
Number of pages7
ISBN (Electronic)9781538679265
DOIs
StatePublished - Jul 2019
Event2019 American Control Conference, ACC 2019 - Philadelphia, United States
Duration: Jul 10 2019Jul 12 2019

Publication series

NameProceedings of the American Control Conference
Volume2019-July
ISSN (Print)0743-1619

Conference

Conference2019 American Control Conference, ACC 2019
Country/TerritoryUnited States
CityPhiladelphia
Period7/10/197/12/19

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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