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 optimization problem reliably
• postprocessing the solution and performing quality checks

At the moment, the main focus of the toolbox are rigid-wing, lift-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.

1. Get a local copy of the latest awebox release:

   git clone https://github.com/awebox/awebox.git
   

2. Install CasADI version 3.4.5 for Python 3, following these installation instructions.

3. In order to get the HSL solvers and render them visible to CasADi, follow these instructions.

Add awebox to the PYTHONPATH environment variable (add those lines to your .bashrc or .zshrc to set the paths permanently).

export PYTHONPATH=<path_to_awebox_root_folder>:$PYTHONPATH

To run one of the examples from the awebox root folder:

python3 examples/single_kite_lift_mode_simple.py

For an overview of the different (user and non-user) options, first have a look at the examples.
An exhaustive overview can be found in awebox/opts/default.py, where all the default options are set.
In order to alter non-user options: generate the Options-object with internal access rights switched on:

import awebox as awe
options = awe.Options(internal_access = True)

and set the according fields in the Options-subdicts to the desired values.

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)

Operational Regions of a Multi-Kite AWE System
R. Leuthold, J. De Schutter, E Malz, G. Licitra, S. Gros, M. Diehl
European Control Conference (ECC) 2018

Optimal Control for Multi-Kite Emergency Trajectories
T. Bronnenmeyer (Masters thesis)
University of Stuttgart 2018

Induction models
Engineering Wake Induction Model For Axisymmetric Multi-Kite Systems
R. Leuthold, C. Crawford, S. Gros, M. Diehl
Wake Conference 2019 (accepted)

Point-mass model
Airborne Wind Energy Based on Dual Airfoils
M. Zanon, S. Gros, J. Andersson, M. Diehl
IEEE Transactions on Control Systems Technology 2013

Homotopy strategy
A Relaxation Strategy for the Optimization of Airborne Wind Energy Systems
S. Gros, M. Zanon, M. Diehl
Proceedings of the European Control Conference (ECC) 2013

Trajectory optimization
Numerical Trajectory Optimization for Airborne Wind Energy Systems Described by High Fidelity Aircraft Models
G. Horn, S. Gros, M. Diehl
Airborne Wind Energy 2013

IPOPT
On the Implementation of a Primal-Dual Interior Point Filter Line Search Algorithm for Large-Scale Nonlinear Programming
A. Wächter, L.T. Biegler
Mathematical Programming 106 (2006) 25-57

CasADi
CasADi - A software framework for nonlinear optimization and optimal control
J.A.E. Andersson, J. Gillis, G. Horn, J.B. Rawlings, M. Diehl
Mathematical Programming Computation, 2018