Python library that implements DeePC: Data-Enabled Predictive Control
A wrapped package for Data-enabled predictive control (DeePC) implementation. Including DeePC and Robust DeePC design with multiple objective functions.
[L4DC 2025] Automatic hyperparameter tuning for DeePC. Built by Michael Cummins at the Automatic Control Laboratory, ETH Zurich.
This project is source code of paper Deep DeePC: Data-enabled predictive control with low or no online optimization using deep learning by X. Zhang, K. Zhang, Z. Li, and X. Yin. The objective of this work is to learn the DeePC operator using a neural network and bypass online optimization of conventional DeePC for efficient online implementation.
Learning-Based Efficient Approximation of Data-Enabled Predictive Control
This repository contains simulation codes generated for the paper titled "Comparative Analysis of Data-Driven Predictive Control Strategies," published in the 9th International Conference on Control, Instrumentation, and Automation in 2023. The paper is authored by Sohrab Rezaei & Ali Khaki-Sedigh.
This code can be used to reproduce the results in our paper ``Floodgates up to contain the DeePC and limit extrapolation''.
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