Python library that implements DeePC: Data-Enabled Predictive Control

J. Berberich, J. Köhler, M. A. Müller and F. Allgöwer, "Data-Driven Model Predictive Control With Stability and Robustness Guarantees," in IEEE Transactions on Automatic Control, vol. 66, no. 4, pp. 1702-1717, April 2021, doi: 10.1109/TAC.2020.3000182.

A wrapped package for Data-enabled predictive control (DeePC) implementation. Including DeePC and Robust DeePC design with multiple objective functions.

Virtual Reference Feedback Tuning (VRFT) Python Library - Alessio Russo (alessior@kth.se)

Z. Sun, Q. Wang, J. Pan and Y. Xia, "Data-Driven MPC for Linear Systems using Reinforcement Learning," 2021 China Automation Congress (CAC), Beijing, China, 2021, pp. 394-399, doi: 10.1109/CAC53003.2021.9728233.

Python library that implements ZPC: Zonotopic Data-Driven Predictive Control.

Automatic hyperparameter tuning for DeePC. Built by Michael Cummins at the Automotaic Control Laboratory, ETH Zurich.

Tube-Based Zonotopic Data Driven Predictive Control

Code for the paper Analysis and Detectability of Offline Data Poisoning Attacks on Linear Systems.

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.

A Python implementation of a Direct Data-Driven Model Predictive Control (MPC) controller for Linear Time-Invariant (LTI) systems, based on the work of J. Berberich et al. presented in the paper "Data-Driven Model Predictive Control With Stability and Robustness Guarantees".

Efficient Computation of Lyapunov Functions Using Deep Neural Networks for the Assessment of Stability in Controller Design

Particle Gibbs-based optimal control with performance guarantees for unknown systems with latent states

Code for journal publication 10.1109/OJCSYS.2023.3291596

This is the Julia implementation of the behavioral control DeePC algorithm.

Code for my project on 'Neural system identification and control for Formula Student Driverless cars'

Data-driven control examples

Virtual Reference Feedback Tuning (VRFT) python library forked from Alessio Russo.