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.
Inflow_original.xlsx (53 KB)c3e6328964aaf988c89209470fa9ccd9Inflow_wavelet.xlsx (47 KB)7391fa6f2fa06035177b57256b7883a4LV_curve.csv (697 Bytes)
" authors: - family-names: Abraham given-names: Edo - family-names: Koo given-names: Ja-Ho orcid: "https://orcid.org/0000-0001-7100-8518" - family-names: Solomatine given-names: Dimitri - family-names: Jonoski given-names: Andreja orcid: "https://orcid.org/0000-0002-0183-4168" title: "Code underlying: Implementation of explicit and switched MPC using data-driven surrogate models" keywords: version: 1 identifiers: - type: doi value: 10.4121/b6dd9d97-118d-406e-867d-b821fb6d08d4.v1 license: CC BY 4.0 date-released: 2025-02-25