https://github.com/wzjoriv/hion
Hion (/ˈiː.ɒn/): Hamiltonian-Informed Optimal Neural Control.
https://github.com/wzjoriv/hion
control-systems control-theory machine-learning mpc-control neural-control optimal-control
Last synced: 4 months ago
JSON representation
Hion (/ˈiː.ɒn/): Hamiltonian-Informed Optimal Neural Control.
- Host: GitHub
- URL: https://github.com/wzjoriv/hion
- Owner: wzjoriv
- Created: 2022-12-17T00:33:15.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-08-16T09:08:58.000Z (10 months ago)
- Last Synced: 2025-09-20T07:41:25.140Z (9 months ago)
- Topics: control-systems, control-theory, machine-learning, mpc-control, neural-control, optimal-control
- Language: Jupyter Notebook
- Homepage:
- Size: 4.73 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Hamiltonian-Informed Optimal Neural (Hion) Controller
Author: Josue N Rivera
---
**Paper:** "Receding Hamiltonian-Informed Optimal Control and State Estimation for Continuous Closed-Loop Dynamical Systems"
This projects implements Hamiltonian-Informed Optimal Neural (Hion) controllers, a novel class of neural network-based controllers for dynamical systems and explicit non-linear model-predictive control. Hion controllers estimate future states and develop an optimal control strategy using Pontryagin’s Maximum Principle. The proposed framework, along with our Taylored Multi-Faceted Approach for Neural ODE and Optimal Control (T-mano) architecture, allows for custom transient behavior, predictive control, and closed-loop feed back, addressing limitations of existing methods. Comparative analyses with established model-predictive controllers revealed Hion controllers’ superior optimality and tracking ca
pabilities. Optimal control strategies are also demonstrated for both linear and non-linear dynamical systems
### Demos
* [`Compare MPCs`](https://github.com/wzjoriv/Hion/blob/main/docs/demos/Compare%20MPCs/presentation.ipynb)
## Development
### Train
```bash
python train-t-mano.py -c "configs/config.linear.json"
```
### Test
```bash
python test-t-mano.py -c "logs/checkpoints/linear(controller).checkpoint.pth"
```
## Citation
```bibtex
@article{rivera2024receding,
title={Receding Hamiltonian-Informed Optimal Neural Control and State Estimation for Closed-Loop Dynamical Systems},
author={Rivera, Josue N and Sun, Dengfeng},
journal={arXiv preprint arXiv:2411.01297},
year={2024}
}
@phdthesis{rivera2024multi,
author = "Josue N Rivera",
title = "{Multi-Scale Design and Control of Complex Advanced UAV Systems}",
year = "2024",
month = "12",
url = "https://hammer.purdue.edu/articles/thesis/Multi-Scale_Design_and_Control_of_Complex_Advanced_UAV_Systems/27937332",
doi = "10.25394/PGS.27937332.v1"
}
```