Analysis and design of model predictive control frameworks for dynamic operation

An overview

authored by: Johannes Köhler, Matthias A. Müller, Frank Allgöwer
Abstract: This article provides an overview of model predictive control (MPC) frameworks for dynamic operation of nonlinear constrained systems. Dynamic operation is often an integral part of the control objective, ranging from tracking of reference signals to the general economic operation of a plant under online changing time-varying operating conditions. We focus on the particular challenges that arise when dealing with such more general control goals and present methods that have emerged in the literature to address these issues. The goal of this article is to present an overview of the state-of-the-art techniques, providing a diverse toolkit to apply and further develop MPC formulations that can handle the challenges intrinsic to dynamic operation. We also critically assess the applicability of the different research directions, discussing limitations and opportunities for further research.
Organisation(s): Institute of Automatic Control
External Organisation(s): ETH Zurich
University of Stuttgart
Type: Review article
Journal: Annual reviews in control
Volume: 57
No. of pages: 25
ISSN: 1367-5788
Publication date: 2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Software, Control and Systems Engineering
Electronic version(s): https://doi.org/10.1016/j.arcontrol.2023.100929 (Access: Open)

BibTeX

@article{cb300bd8a1864486b11e723b6c643d9c,
title = "Analysis and design of model predictive control frameworks for dynamic operation: An overview",
abstract = "This article provides an overview of model predictive control (MPC) frameworks for dynamic operation of nonlinear constrained systems. Dynamic operation is often an integral part of the control objective, ranging from tracking of reference signals to the general economic operation of a plant under online changing time-varying operating conditions. We focus on the particular challenges that arise when dealing with such more general control goals and present methods that have emerged in the literature to address these issues. The goal of this article is to present an overview of the state-of-the-art techniques, providing a diverse toolkit to apply and further develop MPC formulations that can handle the challenges intrinsic to dynamic operation. We also critically assess the applicability of the different research directions, discussing limitations and opportunities for further research.",
keywords = "Closed-loop stability, Dynamic system operation, Economic MPC, Model predictive control (MPC), MPC without stabilizing terminal cost, Tracking MPC",
author = "Johannes K{\"o}hler and M{\"u}ller, {Matthias A.} and Frank Allg{\"o}wer",
note = "Funding Information: Johannes K{\"o}hler was supported by the Swiss National Science Foundation under NCCR Automation (grant agreement 51NF40 180545 ). ",
year = "2024",
doi = "10.1016/j.arcontrol.2023.100929",
language = "English",
volume = "57",
journal = "Annual reviews in control",
issn = "1367-5788",
publisher = "Elsevier Ltd.",
}