awebox is a Python toolbox for modelling and optimal control of multiple-kite systems for Airborne Wind Energy (AWE). It provides interfaces that aim to take away from the user the burden of
- generating optimization-friendly system dynamics for different combinations of modeling options.
- formulating optimal control problems for common multi-kite trajectory types.
- solving the trajectory optimization problem reliably
- postprocessing and visualizing the solution and performing quality checks
- tracking MPC design and handling for offline closed-loop simulations
The main focus of the toolbox are rigid-wing, lift- and drag-mode multiple-kite systems.
awebox runs on Python 3. It depends heavily on the modeling language CasADi, which is a symbolic framework for algorithmic differentiation. CasADi also provides the interface to the NLP solver IPOPT.
It is optional but highly recommended to use HSL linear solvers as a plugin with IPOPT.
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Get a local copy of the latest
aweboxrelease:git clone https://github.com/awebox/awebox.git -
Run the install script:
cd awebox/ python3 setup.py -
In order to get the HSL solvers and render them visible to CasADi, follow these instructions. Additional installation instructions can be found here.
To run one of the examples from the awebox root folder:
python3 examples/single_kite_lift_mode_simple.py
This software has been developed in collaboration with the company Kiteswarms Ltd. The company has also supported the project through research funding.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642682 (AWESCO)
Please use the following citation:
"awebox: Modelling and optimal control of single- and multiple-kite systems for airborne wind energy. https://github.com/awebox/awebox"
Optimal Control of Stacked Multi-Kite Systems for Utility-Scale Airborne Wind Energy
De Schutter et al. / IEEE Conference on Decision and Control (CDC) 2019
Wake Characteristics of Pumping Mode Airborne Wind Energy Systems
Haas et al. / Journal of Physics: Conference Series 2019
Operational Regions of a Multi-Kite AWE System
Leuthold et al. / European Control Conference (ECC) 2018
Optimal Control for Multi-Kite Emergency Trajectories
Bronnenmeyer (Masters thesis) / University of Stuttgart 2018
Induction models
Engineering Wake Induction Model For Axisymmetric Multi-Kite Systems
Leuthold et al. / Wake Conference 2019
Point-mass model
Airborne Wind Energy Based on Dual Airfoils
Zanon et al. / IEEE Transactions on Control Systems Technology 2013
Homotopy strategy
A Relaxation Strategy for the Optimization of Airborne Wind Energy Systems
Gros et al. / European Control Conference (ECC) 2013
Trajectory optimization
Numerical Trajectory Optimization for Airborne Wind Energy Systems Described by High Fidelity Aircraft Models
Horn et al. / Airborne Wind Energy 2013
IPOPT
On the Implementation of a Primal-Dual Interior Point Filter Line Search Algorithm for Large-Scale Nonlinear Programming
Wächter et al. / Mathematical Programming 106 (2006) 25-57
CasADi
CasADi - A software framework for nonlinear optimization and optimal control
Andersson et al. / Mathematical Programming Computation 2018