TY - GEN
T1 - An iterative learning approach for online flight path optimization for tethered energy systems undergoing cyclic spooling motion
AU - Cobb, Mitchell
AU - Barton, Kira
AU - Fathy, Hosam
AU - Vermillion, Chris
N1 - Funding Information:
This research was supported by National Science Foundation award number 1727779, entitled “Collaborative Research: An Economic Iterative Learning Control Framework with Application to Airborne Wind Energy Harvesting”, as well as award number 1538369, titled “Collaborative Research: Self-Adjusting Periodic Optimal Control with Application to Energy-Harvesting Flight”.
Publisher Copyright:
© 2019 American Automatic Control Council.
PY - 2019/7
Y1 - 2019/7
N2 - 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.
AB - 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.
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U2 - 10.23919/acc.2019.8814773
DO - 10.23919/acc.2019.8814773
M3 - Conference contribution
AN - SCOPUS:85072286624
T3 - Proceedings of the American Control Conference
SP - 2164
EP - 2170
BT - 2019 American Control Conference, ACC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 American Control Conference, ACC 2019
Y2 - 10 July 2019 through 12 July 2019
ER -