Paper: Deep DeePC

This project is the 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 the online optimization of conventional DeePC for efficient online implementation.

If you are using this work in your research or teaching, please include explicit acknowledge of this work and of our paper.

Please check the sections of License and Citation at the end.

Paper：link

Source code: link

Developed QYtool toolbox

A wrapped useful tool package designed for research purposes, tailored specifically for my own research needs.

Introduction: link

PyPI site: link

Developed modlinear toolbox

A wrapped package to linearize the nonlinear continuous/discrete model. Including numerical and symbolic calculations.

Github project: link

PyPI: link

Upload package to PyPI

Introduction to how to upload your own package project to PyPI.

Developed deepctools toolbox

Introduction to my developed deepctools toolbox for data-enabled predictive control.

Github project: link

PyPI site: link

Introduction to Jekyll

Try to learn the Jekyll and take some notes

MATLAB plot functions

Some useful information on MATLAB plot

Introduction to CasADi

Notes on CasADi, especially the usage of variables in CasADi

Daily life

Shot by GRIIIx.

My logo

My logo artworks.

Black and White

Some black ideas.

Messiness

Messy plots.