Holohover - SysID

This repository provides the code accompanying the paper On identifying the non-linear dynamics of a hovercraft using an end-to-end deep learning approach.

Getting Started

All configuration is done through the params.json file. It specifies the data source, learning parameters, and initial model parameters.

Preprocessing Data

Before learning a new mode, the data has to be preprocessed. To do so, create a new folder in the experiments folder, add the recorded mcap bag, change the data/experiment field in params.json, and run

python3 run_preprocessing.py

Learning a Model

Change the params.json file accordingly and run

python3 run_learning.py

Datasets and Models from the Paper

The data used for training is 2023_10_18-11_28_12_sysid_h1_old and the data used for validation is 2023_11_22-11_57_44_sysid_h1_old. The relevant models are located in models/paper and the RMSE calculation is done in paper_results.ipynb. The control experiment and analysis can be found in control_experiment.

Citing our Work

To cite our work in other academic papers, please use the following BibTex entry:

@article{schwan2024,
title = {On identifying the non-linear dynamics of a hovercraft using an end-to-end deep learning approach.},
journal = {IFAC-PapersOnLine},
volume = {58},
number = {15},
pages = {289-294},
year = {2024},
note = {20th IFAC Symposium on System Identification SYSID 2024},
issn = {2405-8963},
doi = {https://doi.org/10.1016/j.ifacol.2024.08.543},
url = {https://www.sciencedirect.com/science/article/pii/S2405896324013235},
author = {R. Schwan and N. Schmid and E. Chassaing and K. Samaha and C.N. Jones}
}