Python library that implements DeePC: Data-Enabled Predictive Control
[L4DC 2025] Automatic hyperparameter tuning for DeePC. Built by Michael Cummins at the Automatic Control Laboratory, ETH Zurich.
A wrapped package for Data-enabled predictive control (DeePC) implementation. Including DeePC and Robust DeePC design with multiple objective functions.
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
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