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Environmentally Adaptive Iterative L...

Reed, James Christopher.

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Environmentally Adaptive Iterative Learning for Performance Optimization of Tethered Energy Systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Environmentally Adaptive Iterative Learning for Performance Optimization of Tethered Energy Systems./
Author:	Reed, James Christopher.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	157 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
Contained By:	Dissertations Abstracts International85-03B.
Subject:	Alternative energy. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30563869
ISBN:	9798380262675

Environmentally Adaptive Iterative Learning for Performance Optimization of Tethered Energy Systems.
Reed, James Christopher.

Environmentally Adaptive Iterative Learning for Performance Optimization of Tethered Energy Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 157 p.

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

Thesis (Ph.D.)--North Carolina State University, 2023.

This item must not be sold to any third party vendors.

Over the past several years, there has been significant interest in the research and development of devices capable of harnessing the vast wind and water current energy sources available in the United States and across the globe. Energy-harvesting kite systems, having both underwater and airborne variants, replace the towers of towered turbines with a tether, and large turbine blades with a maneuverable, high lift-to-drag ratio wing, which flies in figure-eight motions. For these kite systems to live up to their potential and outperform existing towered systems, effective power augmenting control techniques are necessary that are adaptable to changing wind and water current environments.This dissertation first focuses on evaluating the stability of periodic figure-8 orbits under flow disturbances, then turns its attention to optimizing those orbits under a varying environment. Floquet and stroboscopic intersection analysis were used to establish the orbital stability of the kite system. With the ability to perform stable cycles established, these orbits were optimized using iterative learning control (ILC), which is a control method used here to take advantage of the kite system's cyclic nature by learning from previous cycles to improve power performance at future cycles while operating in a changing flow environment. Two ILC methodologies are considered in this work. The first is a metamodel-based approach used to augment the shape of the kite's figure-eight path. This approach was validated in experimental tow tests with a scaled kite model. To implement this methodology on an experimental kite system, the algorithm was adapted to augment the orientation setpoint profiles that the experimental flight controller tracks. The second methodology is a model-based environmentally adaptive library-based ILC approach used to select angle of attack profiles for kite systems to modulate their power production. In this method, a new optimal interpolation methodology and library truncation methodology are developed. To implement the model-based ILC algorithm for the kite system, which has changing cycle times, a new flexible time implementation methodology was developed.To perform orbital stability analyses as well as validate the effectiveness of the ILC methodologies in simulation, a dynamic model of the kite system, a sophisticated spatiotemporally varying environmental model, and a lower-level flight controller were created and validated using tow-based experiments. When combined, these elements represent a sophisticated kite system simulation model. Using this model, as well as the insight gained from it, the ILC algorithms that were developed were tested in both simulations and experiments, demonstrating their effectiveness.

ISBN: 9798380262675Subjects--Topical Terms:

3436775
Alternative energy.

Environmentally Adaptive Iterative Learning for Performance Optimization of Tethered Energy Systems.
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245 1 0 $a Environmentally Adaptive Iterative Learning for Performance Optimization of Tethered Energy Systems.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 157 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
500 $a Advisor: Vermillion, Christopher;Bryant, Matthew;Mazzoleni, Andre;Ferguson, Scott;Pantic, Zeljko.
502 $a Thesis (Ph.D.)--North Carolina State University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Over the past several years, there has been significant interest in the research and development of devices capable of harnessing the vast wind and water current energy sources available in the United States and across the globe. Energy-harvesting kite systems, having both underwater and airborne variants, replace the towers of towered turbines with a tether, and large turbine blades with a maneuverable, high lift-to-drag ratio wing, which flies in figure-eight motions. For these kite systems to live up to their potential and outperform existing towered systems, effective power augmenting control techniques are necessary that are adaptable to changing wind and water current environments.This dissertation first focuses on evaluating the stability of periodic figure-8 orbits under flow disturbances, then turns its attention to optimizing those orbits under a varying environment. Floquet and stroboscopic intersection analysis were used to establish the orbital stability of the kite system. With the ability to perform stable cycles established, these orbits were optimized using iterative learning control (ILC), which is a control method used here to take advantage of the kite system's cyclic nature by learning from previous cycles to improve power performance at future cycles while operating in a changing flow environment. Two ILC methodologies are considered in this work. The first is a metamodel-based approach used to augment the shape of the kite's figure-eight path. This approach was validated in experimental tow tests with a scaled kite model. To implement this methodology on an experimental kite system, the algorithm was adapted to augment the orientation setpoint profiles that the experimental flight controller tracks. The second methodology is a model-based environmentally adaptive library-based ILC approach used to select angle of attack profiles for kite systems to modulate their power production. In this method, a new optimal interpolation methodology and library truncation methodology are developed. To implement the model-based ILC algorithm for the kite system, which has changing cycle times, a new flexible time implementation methodology was developed.To perform orbital stability analyses as well as validate the effectiveness of the ILC methodologies in simulation, a dynamic model of the kite system, a sophisticated spatiotemporally varying environmental model, and a lower-level flight controller were created and validated using tow-based experiments. When combined, these elements represent a sophisticated kite system simulation model. Using this model, as well as the insight gained from it, the ILC algorithms that were developed were tested in both simulations and experiments, demonstrating their effectiveness.
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