Code underlying: Implementation of explicit and switched MPC using data-driven surrogate models

DOI:10.4121/b6dd9d97-118d-406e-867d-b821fb6d08d4.v2
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/b6dd9d97-118d-406e-867d-b821fb6d08d4
Datacite citation style:
Koo, Ja-Ho; Edo Abraham; Solomatine, Dimitri; Jonoski, Andreja (2025): Code underlying: Implementation of explicit and switched MPC using data-driven surrogate models. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/b6dd9d97-118d-406e-867d-b821fb6d08d4.v2
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

choose version:
version 2 - 2025-03-10 (latest)
version 1 - 2025-02-25

Python codes to implement explicit and switched MPC using data-driven surrogate models.

The python files starting with PDMPC are for generating PDMPC results to train surrogate models.

O_results_check and W_results_check files are for arranging results from the explicit MPC surrogate model and switched MPC surrogate model, respectively.

W_ML.py is to build and test the switched MPC surrogate model, and O_DNN_hyper_opt.py is to find the optimal hyperparameters for the explicit MPC surrogate model as well as to train it.

The datasets for this research are included.

History

  • 2025-02-25 first online
  • 2025-03-10 published, posted

Publisher

4TU.ResearchData

Format

.py, .txt, .xlsx

Organizations

IHE Delft, Department of Hydroinformatics and Socio-Technical Innovation
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Water Management
Korea Water Resources Public Corporation (K-water)

DATA

Files (13)