Following repository is a minimalistic environment for experimenting with Control Barrier Functions (CBF).
The classical benchmark cartpole is used as the plant model. To keep it simple no simulator is used; instead, full non-linear dynamics of the cartpole is integrated.
Folder structure (ordered with increasing difficulty):
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acc.py: Most well-known, simple example: adaptive cruise control via CBF-QP. (not related to cartpole) -
cartpole.py: Cartpole dynamics and simulation, all controllers use it as plant -
nominal_control.py: Nominal controllers -
cbf_qp.py: Safety-Critical control with classical CBF, constrain cart velocity
| CBF Enabled | CBF Disabled |
|---|---|
 |
 |
ecbf_qp.py: Exponential CBF with known model, constrain cart positioncbf_qp_learning: Safety-Critical control with model uncertainty, CBF + learning (TODO)ecbf_qp_learning: Exponential CBF with unknown model, ECBF + learning (TODO)cbf_qp_id.py: CBF-QP embedded into inverse dynamics formulation.
- Clone the repository
git clone https://github.com/Berk-Tosun/cbf-cartpole- Install required packages
pip install -r requirements.txtMost python files in this project have a main guard. It means you can directly run them, e.g.:
python cbf_qp.pyThis will generate one of the figures given in the introduction section.
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Recommended introductory presentation: Jason Choi, UC Berkeley. Matching repository of the talk, where you can get the slides and the mentioned matlab code.
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Presentation by the main author of the CBF framework, Prof. Aaron Ames.
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Another introductory presentation: Air Lab Summer School 2020.
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Sample hardware application: Multi-Layered Safety for Legged Robots via Control Barrier Functions and Model Predictive Control. Its presentation from ICRA 2021.