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

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.本代码库实现了基于数据驱动代理模型的显式与切换式模型预测控制（Model Predictive Control，MPC）。以PDMPC开头的Python文件用于生成训练代理模型所需的PDMPC结果。O_results_check与W_results_check文件分别用于整理显式MPC代理模型与切换式MPC代理模型的输出结果。W_ML.py用于构建并测试切换式MPC代理模型；O_DNN_hyper_opt.py则用于确定显式MPC代理模型的最优超参数并完成模型训练。本研究配套的数据集已包含在内。