MPC Solver

A python casadi-based module for nonlinear MPC

This module implements the moving hozion estimation algorithm as described in the author’s book:

M. Alamir, Nonlinear Control of Uncertain Systems: Conventional and Learning-Based alternatives with python, Springer-Nature, 2025.

that will be released shortly in 2025.

The module enables to create the MPC feedback, simulate the resulting closed-loop system and plot the resulting trajectories in closed-loop. The robustness against parametric uncertainties can also be simulated by using different de-tuned vector of parameters in the MPC solver that is different from the one used int he simualted system.

Two different solvers can be used, namely:

Regarding the second option, the gradient is automatically computed using the jacobian utilitiy provided by the casadi framework.

Downloading the module

Please go to the guthub repository and click on the download button

Description of the files

The jupyter notebook entitled Using_mpc_solver_module_PVTOL_example.ipynb provides the example of use of both option on the specific examples of the Planar Vertical Take-Of and Landing (PVTOL) aircraft showing two refulated variable, two control inputs and 6 states.

For more details regarding the available utitlities, refer to the above mentioned book or to the module file mpc_solver.py.

How to cite the algorithms

@Inbook{Alamir2025,
title="Nonlinear Moving-Horizon Extended Observers",
bookTitle="Nonlinear Control of Uncertain Systems: Conventional and Learning-Based Alternatives with python",
year="2025",
publisher="Springer-Nature",
isbn="ISBN-13-978-3031932861"
}