TY - DATA T1 - Code underlying: Implementation of explicit and switched MPC using data-driven surrogate models PY - 2025/02/25 AU - Edo Abraham AU - Ja-Ho Koo AU - Dimitri Solomatine AU - Andreja Jonoski UR - DO - 10.4121/b6dd9d97-118d-406e-867d-b821fb6d08d4.v1 KW - Model Predictive Control KW - Parameterized Dynamic MPC KW - Switched MPC KW - Explicit MPC KW - DNN KW - Surrogate Model N2 -

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)

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