Data-driven Model Predictive Control using MATLAB

TL;DR

This paper reviews data-driven model predictive control methods, including linear and nonlinear approaches, highlighting recent advancements and implementation techniques using MATLAB, and discusses future research directions.

Contribution

It provides a comprehensive overview of data-driven MPC methodologies, emphasizing recent developments and practical implementation strategies.

Findings

Overview of data-driven LMPC and NMPC approaches

Implementation techniques using MATLAB for various methods

Discussion of recent advancements and future research directions

Abstract

This paper presents a comprehensive overview of data-driven model predictive control, highlighting state-of-the-art methodologies and their numerical implementation. The discussion begins with a brief review of conventional model predictive control (MPC), which discusses both linear MPC (LMPC) and nonlinear MPC (NMPC). This is followed by a section on data-driven LMPC, outlining fundamental concepts and the implementation of various approaches, including subspace predictive control and prediction error methods. Subsequently, the focus shifts to data-driven NMPC, emphasizing approaches based on neural network models. The paper concludes with a review of recent advancements in data-driven MPC and explores potential directions for future research.