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.

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

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

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

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

Tube-Based Zonotopic Data Driven Predictive Control

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

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.

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'

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

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

Data-driven control examples

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