Transient Performance of MPC for Tracking

Research output: Contribution to journalArticleResearchpeer review

Authors

Research Organisations

External Research Organisations

  • University of Stuttgart
  • University of Bayreuth
View graph of relations

Details

Original languageEnglish
Pages (from-to)2545-2550
Number of pages6
JournalIEEE Control Systems Letters
Volume7
Publication statusPublished - 20 Jun 2023

Abstract

We analyse the closed-loop performance of a model predictive control (MPC) for tracking formulation with artificial references. It has been shown that such a scheme guarantees closed-loop stability and recursive feasibility for any externally supplied reference, even if it is unreachable or time-varying. The basic idea is to consider an artificial reference as an additional decision variable and to formulate generalised terminal ingredients with respect to it. In addition, its offset is penalised in the MPC optimisation problem, leading to closed-loop convergence to the best reachable reference. In this paper, we provide a transient performance bound on the closed loop using MPC for tracking. We employ mild assumptions on the offset cost and scale it with the prediction horizon. In this case, an increasing horizon in MPC for tracking recovers the infinite horizon optimal solution.

Keywords

    Costs, Model predictive control, nonlinear systems, Nonlinear systems, Optimization, Predictive control, set-point tracking, Standards, Tracking loops, Transient analysis, transient performance

ASJC Scopus subject areas

Cite this

Transient Performance of MPC for Tracking. / Kohler, Matthias; Krugel, Lisa; Grune, Lars et al.
In: IEEE Control Systems Letters, Vol. 7, 20.06.2023, p. 2545-2550.

Research output: Contribution to journalArticleResearchpeer review

Kohler, M, Krugel, L, Grune, L, Muller, MA & Allgower, F 2023, 'Transient Performance of MPC for Tracking', IEEE Control Systems Letters, vol. 7, pp. 2545-2550. https://doi.org/10.48550/arXiv.2303.10006, https://doi.org/10.1109/LCSYS.2023.3287798
Kohler, M., Krugel, L., Grune, L., Muller, M. A., & Allgower, F. (2023). Transient Performance of MPC for Tracking. IEEE Control Systems Letters, 7, 2545-2550. https://doi.org/10.48550/arXiv.2303.10006, https://doi.org/10.1109/LCSYS.2023.3287798
Kohler M, Krugel L, Grune L, Muller MA, Allgower F. Transient Performance of MPC for Tracking. IEEE Control Systems Letters. 2023 Jun 20;7:2545-2550. doi: 10.48550/arXiv.2303.10006, 10.1109/LCSYS.2023.3287798
Kohler, Matthias ; Krugel, Lisa ; Grune, Lars et al. / Transient Performance of MPC for Tracking. In: IEEE Control Systems Letters. 2023 ; Vol. 7. pp. 2545-2550.
Download
@article{883a859af54841fead6705931ab40c79,
title = "Transient Performance of MPC for Tracking",
abstract = "We analyse the closed-loop performance of a model predictive control (MPC) for tracking formulation with artificial references. It has been shown that such a scheme guarantees closed-loop stability and recursive feasibility for any externally supplied reference, even if it is unreachable or time-varying. The basic idea is to consider an artificial reference as an additional decision variable and to formulate generalised terminal ingredients with respect to it. In addition, its offset is penalised in the MPC optimisation problem, leading to closed-loop convergence to the best reachable reference. In this paper, we provide a transient performance bound on the closed loop using MPC for tracking. We employ mild assumptions on the offset cost and scale it with the prediction horizon. In this case, an increasing horizon in MPC for tracking recovers the infinite horizon optimal solution.",
keywords = "Costs, Model predictive control, nonlinear systems, Nonlinear systems, Optimization, Predictive control, set-point tracking, Standards, Tracking loops, Transient analysis, transient performance",
author = "Matthias Kohler and Lisa Krugel and Lars Grune and Muller, {Matthias A.} and Frank Allgower",
note = "Funding Information: This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Grant AL 316/11-2-244600449 and Grant GR 1569/13-2-244602989, and in part by the Germany's Excellence Strategy under Grant EXC 2075-390740016.",
year = "2023",
month = jun,
day = "20",
doi = "10.48550/arXiv.2303.10006",
language = "English",
volume = "7",
pages = "2545--2550",

}

Download

TY - JOUR

T1 - Transient Performance of MPC for Tracking

AU - Kohler, Matthias

AU - Krugel, Lisa

AU - Grune, Lars

AU - Muller, Matthias A.

AU - Allgower, Frank

N1 - Funding Information: This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Grant AL 316/11-2-244600449 and Grant GR 1569/13-2-244602989, and in part by the Germany's Excellence Strategy under Grant EXC 2075-390740016.

PY - 2023/6/20

Y1 - 2023/6/20

N2 - We analyse the closed-loop performance of a model predictive control (MPC) for tracking formulation with artificial references. It has been shown that such a scheme guarantees closed-loop stability and recursive feasibility for any externally supplied reference, even if it is unreachable or time-varying. The basic idea is to consider an artificial reference as an additional decision variable and to formulate generalised terminal ingredients with respect to it. In addition, its offset is penalised in the MPC optimisation problem, leading to closed-loop convergence to the best reachable reference. In this paper, we provide a transient performance bound on the closed loop using MPC for tracking. We employ mild assumptions on the offset cost and scale it with the prediction horizon. In this case, an increasing horizon in MPC for tracking recovers the infinite horizon optimal solution.

AB - We analyse the closed-loop performance of a model predictive control (MPC) for tracking formulation with artificial references. It has been shown that such a scheme guarantees closed-loop stability and recursive feasibility for any externally supplied reference, even if it is unreachable or time-varying. The basic idea is to consider an artificial reference as an additional decision variable and to formulate generalised terminal ingredients with respect to it. In addition, its offset is penalised in the MPC optimisation problem, leading to closed-loop convergence to the best reachable reference. In this paper, we provide a transient performance bound on the closed loop using MPC for tracking. We employ mild assumptions on the offset cost and scale it with the prediction horizon. In this case, an increasing horizon in MPC for tracking recovers the infinite horizon optimal solution.

KW - Costs

KW - Model predictive control

KW - nonlinear systems

KW - Nonlinear systems

KW - Optimization

KW - Predictive control

KW - set-point tracking

KW - Standards

KW - Tracking loops

KW - Transient analysis

KW - transient performance

UR - http://www.scopus.com/inward/record.url?scp=85163492382&partnerID=8YFLogxK

U2 - 10.48550/arXiv.2303.10006

DO - 10.48550/arXiv.2303.10006

M3 - Article

AN - SCOPUS:85163492382

VL - 7

SP - 2545

EP - 2550

JO - IEEE Control Systems Letters

JF - IEEE Control Systems Letters

ER -

By the same author(s)