%0 Generic
%A Koo, Ja-Ho
%A Abraham, Edo
%A Solomatine, Dimitri
%A Jonoski, Andreja
%D 2025
%T Code underlying: Implementation of explicit and switched MPC using data-driven surrogate models
%U 
%R 10.4121/b6dd9d97-118d-406e-867d-b821fb6d08d4.v2
%K Model Predictive Control
%K Parameterized Dynamic MPC
%K Switched MPC
%K Explicit MPC
%K DNN
%K Surrogate Model
%X <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>
%I 4TU.ResearchData