Cooperative Tuning of Multi-Agent Optimal Control Systems

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

Dependencies

Dependency:

• CasADi
• cyipopt
• NumPy
• Matplotlib

Installation

$ pip3 install numpy matplotlib casadi cyipopt
$ cd
$ git clone https://github.com/zehuilu/Cooperative-Tuning-of-Multi-Agent-Optimal-Control-Systems.git

Usage

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.

Example

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