Constrained nonlinear output regulation using model predictive control

Research output: Contribution to journalArticleResearchpeer review

Authors

Research Organisations

External Research Organisations

  • ETH Zurich
  • University of Stuttgart
View graph of relations

Details

Original languageEnglish
Pages (from-to)2419-2434
Number of pages16
JournalIEEE Transactions on Automatic Control
Volume67
Issue number5
Publication statusPublished - 18 May 2021

Abstract

We present a model predictive control (MPC) framework to solve the constrained nonlinear output regulation problem. The main feature of the proposed framework is that the application does not require the solution to classical regulator (Francis-Byrnes-Isidori) equations or any other offline design procedure. In particular, the proposed formulation simply minimizes the predicted output error, possibly with some input regularization. Instead of using terminal cost/sets or a positive definite stage cost as is standard in MPC theory, we build on the theoretical results by Grimm et al. 2005 using a detectability notion. The proposed formulation is applicable if the constrained nonlinear regulation problem is (strictly) feasible, the plant is incrementally stabilizable and incrementally input-output to state stable (i-IOSS/detectable). We show that for minimum phase systems such a design ensures exponential stability of the regulator manifold. We also provide a design procedure in case of unstable zero dynamics using an incremental input regularization and a nonresonance condition. The theoretical results are illustrated with an example involving offset free tracking.

Keywords

    Constrained control, Disturbance rejection, Incremental system properties, Mathematical model, Minimum phase, Nonresonance condition, Output regulation, Predictive control, Predictive control for nonlinear systems, Regulation, Regulators, Steady-state, Trajectory, Trajectory tracking, Zero dynamics

ASJC Scopus subject areas

Cite this

Constrained nonlinear output regulation using model predictive control. / Koehler, Johannes; Muller, Matthias A.; Allgower, Frank.
In: IEEE Transactions on Automatic Control, Vol. 67, No. 5, 18.05.2021, p. 2419-2434.

Research output: Contribution to journalArticleResearchpeer review

Koehler J, Muller MA, Allgower F. Constrained nonlinear output regulation using model predictive control. IEEE Transactions on Automatic Control. 2021 May 18;67(5):2419-2434. doi: 10.1109/TAC.2021.3081080
Download
@article{547f47eb7a05465c955a55204d0fcf01,
title = "Constrained nonlinear output regulation using model predictive control",
abstract = "We present a model predictive control (MPC) framework to solve the constrained nonlinear output regulation problem. The main feature of the proposed framework is that the application does not require the solution to classical regulator (Francis-Byrnes-Isidori) equations or any other offline design procedure. In particular, the proposed formulation simply minimizes the predicted output error, possibly with some input regularization. Instead of using terminal cost/sets or a positive definite stage cost as is standard in MPC theory, we build on the theoretical results by Grimm et al. 2005 using a detectability notion. The proposed formulation is applicable if the constrained nonlinear regulation problem is (strictly) feasible, the plant is incrementally stabilizable and incrementally input-output to state stable (i-IOSS/detectable). We show that for minimum phase systems such a design ensures exponential stability of the regulator manifold. We also provide a design procedure in case of unstable zero dynamics using an incremental input regularization and a nonresonance condition. The theoretical results are illustrated with an example involving offset free tracking.",
keywords = "Constrained control, Disturbance rejection, Incremental system properties, Mathematical model, Minimum phase, Nonresonance condition, Output regulation, Predictive control, Predictive control for nonlinear systems, Regulation, Regulators, Steady-state, Trajectory, Trajectory tracking, Zero dynamics",
author = "Johannes Koehler and Muller, {Matthias A.} and Frank Allgower",
note = "Publisher Copyright: {\textcopyright} 1963-2012 IEEE.",
year = "2021",
month = may,
day = "18",
doi = "10.1109/TAC.2021.3081080",
language = "English",
volume = "67",
pages = "2419--2434",
journal = "IEEE Transactions on Automatic Control",
issn = "0018-9286",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "5",

}

Download

TY - JOUR

T1 - Constrained nonlinear output regulation using model predictive control

AU - Koehler, Johannes

AU - Muller, Matthias A.

AU - Allgower, Frank

N1 - Publisher Copyright: © 1963-2012 IEEE.

PY - 2021/5/18

Y1 - 2021/5/18

N2 - We present a model predictive control (MPC) framework to solve the constrained nonlinear output regulation problem. The main feature of the proposed framework is that the application does not require the solution to classical regulator (Francis-Byrnes-Isidori) equations or any other offline design procedure. In particular, the proposed formulation simply minimizes the predicted output error, possibly with some input regularization. Instead of using terminal cost/sets or a positive definite stage cost as is standard in MPC theory, we build on the theoretical results by Grimm et al. 2005 using a detectability notion. The proposed formulation is applicable if the constrained nonlinear regulation problem is (strictly) feasible, the plant is incrementally stabilizable and incrementally input-output to state stable (i-IOSS/detectable). We show that for minimum phase systems such a design ensures exponential stability of the regulator manifold. We also provide a design procedure in case of unstable zero dynamics using an incremental input regularization and a nonresonance condition. The theoretical results are illustrated with an example involving offset free tracking.

AB - We present a model predictive control (MPC) framework to solve the constrained nonlinear output regulation problem. The main feature of the proposed framework is that the application does not require the solution to classical regulator (Francis-Byrnes-Isidori) equations or any other offline design procedure. In particular, the proposed formulation simply minimizes the predicted output error, possibly with some input regularization. Instead of using terminal cost/sets or a positive definite stage cost as is standard in MPC theory, we build on the theoretical results by Grimm et al. 2005 using a detectability notion. The proposed formulation is applicable if the constrained nonlinear regulation problem is (strictly) feasible, the plant is incrementally stabilizable and incrementally input-output to state stable (i-IOSS/detectable). We show that for minimum phase systems such a design ensures exponential stability of the regulator manifold. We also provide a design procedure in case of unstable zero dynamics using an incremental input regularization and a nonresonance condition. The theoretical results are illustrated with an example involving offset free tracking.

KW - Constrained control

KW - Disturbance rejection

KW - Incremental system properties

KW - Mathematical model

KW - Minimum phase

KW - Nonresonance condition

KW - Output regulation

KW - Predictive control

KW - Predictive control for nonlinear systems

KW - Regulation

KW - Regulators

KW - Steady-state

KW - Trajectory

KW - Trajectory tracking

KW - Zero dynamics

UR - http://www.scopus.com/inward/record.url?scp=85107227245&partnerID=8YFLogxK

U2 - 10.1109/TAC.2021.3081080

DO - 10.1109/TAC.2021.3081080

M3 - Article

AN - SCOPUS:85107227245

VL - 67

SP - 2419

EP - 2434

JO - IEEE Transactions on Automatic Control

JF - IEEE Transactions on Automatic Control

SN - 0018-9286

IS - 5

ER -

By the same author(s)