wasserstein-nonlinear-mpc
Science Score: 26.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (6.3%) to scientific vocabulary
Last synced: 10 months ago
·
JSON representation
Repository
Basic Info
- Host: GitHub
- Owner: aaronkandel
- Language: MATLAB
- Default Branch: main
- Size: 7.02 MB
Statistics
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Created almost 3 years ago
· Last pushed about 2 years ago
Metadata Files
Readme
Citation
README.md
Wasserstein-Nonlinear-MPC
This is the code corresponding to the case studies in the following paper:
Aaron Kandel and Scott Moura, "Safe Learning MPC with Limited Model Knowledge and Data," conditionally accepted to IEEE Transactions on Control Systems Technology.
This script requires the MATLAB deep learning toolbox. Python versions will be available soon!
Note: Sometimes the AV example will ''false start'' and not move appreciably from the initial point. This is a potential issue in model training, but occurs infrequently. If that scenario occurs, feel free to restart the simulation.
Owner
- Name: Aaron Kandel
- Login: aaronkandel
- Kind: user
- Location: Berkeley, CA
- Company: UC Berkeley
- Repositories: 1
- Profile: https://github.com/aaronkandel