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 high-fidelity system dynamics for different modeling options.
- formulating and solving the trajectory optimization problem efficiently and reliably, also for long time horizons
- postprocessing and visualizing the solution and performing quality checks
- tracking MPC design and solver generation for 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) |
|---|---|
- Ampyx AP2 (6DOF)
- MegAWES (6DOF)
- point-mass model with lift and roll control (3DOF)
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.
-
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
AWEbox has been developed under the supervision of Prof. Dr. Moritz Diehl (University of Freiburg, Germany) and has received financial support from the company Kiteswarms GmbH through an industrial research project as well as from the EU Horizon 2020 programme under the Marie Skłodowska-Curie grant agreement No 642682 (AWESCO) and from the German DFG via Grant No 525018088 (MAWERO).
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
and see also:
Harzer, J,; De Schutter, J.; Diehl, M. Numerical Trajectory Optimization of Airborne Wind Energy Systems With Stroboscopic Averaging Methods, IEEE Control Systems Letters 2025 (9), pp. 703-708. https://doi.org/10.1109/LCSYS.2025.3577225