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.
| Single-kite optimal trajectory | Dual-kite optimal trajectory (reel-out) |
|---|---|
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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 -
Install using pip
pip3 install awebox/ -
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/ampyx_ap2_trajectory.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:
"De Schutter, J.; Leuthold, R.; Bronnenmeyer, T.; Malz, E.; Gros, S.; Diehl, M. AWEbox: An Optimal Control Framework for Single- and Multi-Aircraft Airborne Wind Energy Systems. Energies 2023, 16, 1900. https://doi.org/10.3390/en16041900"