Details
| Original language | English |
|---|---|
| Article number | 100929 |
| Number of pages | 25 |
| Journal | Annual reviews in control |
| Volume | 57 |
| Early online date | 9 Jan 2024 |
| Publication status | Published - 2024 |
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
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Engineering(all)
- Control and Systems Engineering
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In: Annual reviews in control, Vol. 57, 100929, 2024.
Research output: Contribution to journal › Review article › Research › peer review
}
TY - JOUR
T1 - Analysis and design of model predictive control frameworks for dynamic operation
T2 - An overview
AU - Köhler, Johannes
AU - Müller, Matthias A.
AU - Allgöwer, Frank
N1 - Funding Information: Johannes Köhler was supported by the Swiss National Science Foundation under NCCR Automation (grant agreement 51NF40 180545 ).
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Closed-loop stability
KW - Dynamic system operation
KW - Economic MPC
KW - Model predictive control (MPC)
KW - MPC without stabilizing terminal cost
KW - Tracking MPC
UR - http://www.scopus.com/inward/record.url?scp=85182025626&partnerID=8YFLogxK
U2 - 10.1016/j.arcontrol.2023.100929
DO - 10.1016/j.arcontrol.2023.100929
M3 - Review article
AN - SCOPUS:85182025626
VL - 57
JO - Annual reviews in control
JF - Annual reviews in control
SN - 1367-5788
M1 - 100929
ER -