Model-Predictive-Control-for-Linear-Systems-in-Cpp-by-Using-Eigen-Library

IMPORTANT NOTE: First, thoroughly read the license in the file called LICENSE.md!

This repository contains C++ files that explain how to implement the Model Predictive Control (MPC) algorithm for linear systems in C++ by using the Eigen C++ matrix library.

The implemented MPC algorithm is derived and explained in this webpage tutorial:

https://aleksandarhaber.com/model-predictive-control-mpc-tutorial-2-unconstrained-solution-for-linear-systems-and-implementation-in-c-from-scratch-by-using-eigen-c-library/

The YouTube tutorial explaining the derivation and implementation is given below:

https://www.youtube.com/watch?v=fgNz1RE2DG4

TO DO AND TO IMPROVE: PASS BY CONST REFERENCE FOR SPEED.

Explanation of the posted files:

• "ModelPredictiveController.h" is the header file of the class that implements the MPC algorithm

• "ModelPredictiveController.cpp" is the implementation file of the class that implements the MPC algorithm

• "driver_code.cpp" is the driver code for the class that implements the MPC algorithm. You should run this file to generate the results. The results are saved in the comma-separated-value files (csv files). The files store the controlled state trajectory, controlled outputs, computed inputs, and desired trajectory. Also, these files store the lifted system matrices O and M.

• "visualizeResultsPython.py" is the Python file used to plot the results. This file reads the saved csv files and it plots the controlled output trajectory, desired trajectory, and applied control inputs. One of the generated graphs is shown below.