Skip to content

Latest commit

 

History

History
32 lines (25 loc) · 1.44 KB

README.md

File metadata and controls

32 lines (25 loc) · 1.44 KB
Raw

Tracking Control

Codes of a submitted manuscript to Nature Communications.

Model-free tracking control of complex dynamical trajectories with machine learning has been published in Nature Communications!

Requirements

Please download the dataset at https://doi.org/10.5281/zenodo.8044994
The chaotic trajectories should be moved into the folder: read_data. The periodic trajectories are generated in the code

Note that we use a built-in package 'matsplit' in MATLAB. Please click 'Home', choose 'Add-Ons', search this package and install it to run the code.

Example

Run 'main.m' with traj_type = 'circle', you will get the ground truth and tracked trajectories in the picture bellow:

Lorenz

Change traj_type to others to track different trajectories, e.g., traj_type = 'lorenz'.

Citation

This work is available at https://www.nature.com/articles/s41467-023-41379-3, and can be cited with the followling bibtex entry:

@article{zhai2023model,
  title={Model-free tracking control of complex dynamical trajectories with machine learning},
  author={Zhai, Zheng-Meng and Moradi, Mohammadamin and Kong, Ling-Wei and Glaz, Bryan and Haile, Mulugeta and Lai, Ying-Cheng},
  journal={Nature Communications},
  volume={14},
  number={1},
  pages={5698},
  year={2023},
  publisher={Nature Publishing Group UK London}
}