Learning-based Rigid Tube Model Predictive Control

This repository contains code for the article

@article{gao2024learninghomo,
  title={Learning-based Homothetic Tube MPC},
  author={Yulong Gao, Shuhao Yan, Jian Zhou, Mark Cannon},
  year={2024},
  pages={}
} 

Packages for running the code

To run the code you need to install:

CasADi: https://web.casadi.org/;

MPT: https://www.mpt3.org/Main/Installation; (The installation will automatically install Yalmip, which is also necessary for running the code.)

Introduction to the files

See the introductions to the two folders below.

CarFollowingSimulation

This folder contains a subfolder Function, which include the basic functions for running the algorithm, and several scripts. To run the method, you need to first run systemmodel2V.m, which define the parameters offline. After this, you can run main_simulation.m, which compares three method: the proposed learning-based homothetic tube MPC, the learning-based rigid tube MPC [1], and the traditional homothetic tube MPC [2], where these three MPC methods are defined in the scripts HomotheticMPC.m, RigidMPC.m, and TradHomotheicMPC.m, respectively.

RobustSatisfaction

The files in this folder are similar to those in the above. The script main_robustness.m exams the statistical satisfaction of the method in stochastic simulations.

Reference

[1] Gao, Yulong, Shuhao Yan, Jian Zhou, Mark Cannon, Alessandro Abate, and Karl Henrik Johansson. "Learning-based rigid tube model predictive control." In 6th Annual Learning for Dynamics & Control Conference, pp. 492-503. PMLR, 2024.

[2] Kouvaritakis, Basil, and Mark Cannon. "Model predictive control." Switzerland: Springer International Publishing 38 (2016).