Control-based optimization for tethered tidal kite


This submission includes three peer-reviewed (under review) papers from the researchers at North Carolina State University presenting control-based techniques to maximize effectiveness of a tethered tidal kite. Below are the abstracts of each file included in the submission.

Cobb TCST - Iterative learning for kite path optimization.pdf
This paper presents an iterative learning control-based approach for optimizing the flight path geometry of a tethered MHK system. Tethered MHK systems, which replace the tower and turbine of a conventional system with a tether and a lifting body, capture energy by driving a generator with the tension in the tether. By spooling out tether during the high tension portions of cross-current flight and spooling in during low tension portions, net positive energy is generated over one cycle. Because the net energy generation is sensitive to the shape of the flown path, we employ an iterative learning update law to adapt the path shape from one lap to the next. Additionally, we present a realistic system model, along with lower-level path-following and power take-off (PTO) controllers. We then demonstrate the efficacy of our algorithm on this model in both uniform and realistic flow environments.

Siddiqui ACC - Optimal spooling control of kites in variable flow.pdf
This work focuses on the development of an adaptive control strategy that fuses Gaussian process modeling and receding horizon control to ideally manage the tradeoff between exploration (i.e., maintaining an adequate map of the resource) and exploitation (i.e., carrying out a mission, which consists in this work of harvesting the resource). The use of a receding horizon formulation aids in the consideration of limited mobility, which is characteristic of dynamical systems. In this work, we focus on an airborne wind energy (AWE) system as a case study, where the system can vary its elevation angle (tether angle relative to the ground, which trades off higher efficiency with higher-altitude operation) and flight path parameters in order to maximize power output in a wind environment that is changing in space and time. We demonstrate the effectiveness of the proposed approach through a data-driven study on a rigid wing-based AWE system.

Reed ACC - Spatial optimization of kite paths.pdf
This paper presents a technique for maximizing the power production of a tethered marine energy-harvesting kite performing cross-current figure-eight flight in a 3D spatiotemporally varying flow environment. To generate a net positive power output, the kite employs a cyclic spooling method, where the kite is spooled out while flying in high-tension crosscurrent figure-eight flight, then spooled in radially towards the base-station under low tension.

3 Resources

*downloads since 2019

Related Datasets

Datasets associated with the same DOE project
  Submission Name Resources Submitted Status

Additional Info

DOE Project Name: Device Design and Robust Periodic Motion Control of an Ocean Kite System for Marine Hydrokinetic Energy Harvesting
DOE Project Number: EE0008635
DOE Project Lead: Carrie Noonan
Last Updated: 5 months ago
Data from March, 2020
Submitted Dec 4, 2020


North Carolina State University



Publicly accessible License 


Chris Vermillion
North Carolina State University
Mitchell Cobb
North Carolina State University
James Reed
North Carolina State University
Joshua Daniels
North Carolina State University
Ayaz Siddiqui
North Carolina State University
Max Wu
University of Michigan
Hosam Fathy
University of Maryland
Kira Barton
University of Michigan
Michael Muglia
Coastal Studies Institute
Ben Haydon
North Carolina State University


MHK, Marine, Hydrokinetic, energy, power, kite, control, tidal kite, spatial optimization, CEC, cyclic spooling, airborne wind energy, AWE, Gaussian, exploration, exploitation, tethered, power take-off, PTO, model, modeling, cross-current, controller, tension, figure-eight, plant, optimization, spatial, adaptive control, receding horizon, MATLAB, cyclic control, iterative learning, path, fly-gen, ground-gen, generator


Export Citation to RIS
Submission Downloads