This is Cooperative Tuning of Multi-Agent Optimal Control Systems. This paper was accepted by the 61st IEEE Conference on Decision and Control, 2022. The arxiv version is https://arxiv.org/abs/2209.12017
Please cite us if you think this work is helpful. An IEEE version of citation information will be updated once it's available online.
@misc{https://doi.org/10.48550/arxiv.2209.12017,
doi = {10.48550/ARXIV.2209.12017},
url = {https://arxiv.org/abs/2209.12017},
author = {Lu, Zehui and Jin, Wanxin and Mou, Shaoshuai and Anderson, Brian D. O.},
title = {Cooperative Tuning of Multi-Agent Optimal Control Systems},
publisher = {arXiv},
year = {2022},
}
This repo has been tested with:
- Python 3.9.10, macOS 11.4
- python 3.8.5, Ubuntu 20.04.2 LTS
Dependency:
$ pip3 install numpy matplotlib casadi cyipopt
$ cd
$ git clone https://github.com/zehuilu/Cooperative-Tuning-of-Multi-Agent-Optimal-Control-Systems.git
To use coopeartive tuning for a user-defined dynamic system, you need to define a class containing the dynamics of the system and the objective funtion, loss function, etc. You can mimic Unicycle.py
.
NOTE: All the class properties and methods must have the same names as those in Unicycle.py
. Otherwise, other source files won't be able to use these properties and methods.
To test an optimal control with a self-defined dynamic system, see run_oc_Unicycle.py
.
$ cd <MAIN_DIRECTORY>
$ python3 example/run_oc_Unicycle.py
The simulation example in our paper is shown in run_MultiPDP_Unicycle.py
.
$ cd <MAIN_DIRECTORY>
$ python3 example/run_MultiPDP_Unicycle.py