TY - DATA
T1 - Code underlying: Implementation of explicit and switched MPC using data-driven surrogate models
PY - 2025/03/10
AU - Ja-Ho Koo
AU - Edo Abraham
AU - Dimitri Solomatine
AU - Andreja Jonoski
UR - 
DO - 10.4121/b6dd9d97-118d-406e-867d-b821fb6d08d4.v2
KW - Model Predictive Control
KW - Parameterized Dynamic MPC
KW - Switched MPC
KW - Explicit MPC
KW - DNN
KW - Surrogate Model
N2 - <p>Python codes to implement explicit and switched MPC using data-driven surrogate models.</p><p>The python files starting with PDMPC are for generating PDMPC results to train surrogate models.</p><p>O_results_check and W_results_check files are for arranging results from the explicit MPC surrogate model and switched MPC surrogate model, respectively.</p><p>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.</p><p>The datasets for this research are included.</p>
ER -