MPC for linear parameter-varying systems in input-output representation

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

In this paper, we propose a method for model predictive control of linear parameter-varying (LPV) systems described in an input-output (IO) representation and subject to input- and output constraints. By assuming exact knowledge of the future trajectory of the scheduling variable, the on-line computations reduce to the solution of a nominal predictive control problem. An incremental non-minimal state-space representation is used as a prediction model, giving a controller with integral action suitable for tracking piecewise-constant reference signals. Closed-loop asymptotic stability is guaranteed by a terminal cost and terminal set constraint, and the computation of an ellipsoidal terminal set is discussed. Numerical examples demonstrate the properties of the proposed approach. When exact future knowledge of the scheduling variable is not available, we argue and show that good practical performance can be obtained by a scheduling prediction strategy.
LanguageEnglish
Title of host publicationProceedings of the 2016 IEEE International Symposium on Intelligent Control (ISIC), 20-22 september 2016, Buenos Aires, Argentina
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages354-359
DOIs
StatePublished - 2016
Event2016 IEEE International Symposium on Intelligent Control - Buenos Aires, Argentina
Duration: 20 Sep 201622 Sep 2016

Conference

Conference2016 IEEE International Symposium on Intelligent Control
Abbreviated titleISIC
CountryArgentina
CityBuenos Aires
Period20/09/1622/09/16

Fingerprint

Scheduling
Model predictive control
Asymptotic stability
Trajectories
Controllers
Costs

Cite this

Hanema, J., Tóth, R., Lazar, M., & Abbas, H. S. (2016). MPC for linear parameter-varying systems in input-output representation. In Proceedings of the 2016 IEEE International Symposium on Intelligent Control (ISIC), 20-22 september 2016, Buenos Aires, Argentina (pp. 354-359). Piscataway: Institute of Electrical and Electronics Engineers. DOI: 10.1109/ISIC.2016.7579987
Hanema, J. ; Tóth, R. ; Lazar, M. ; Abbas, H.S./ MPC for linear parameter-varying systems in input-output representation. Proceedings of the 2016 IEEE International Symposium on Intelligent Control (ISIC), 20-22 september 2016, Buenos Aires, Argentina. Piscataway : Institute of Electrical and Electronics Engineers, 2016. pp. 354-359
@inproceedings{3dbcadb40d4141fa97ef7f4364952ffa,
title = "MPC for linear parameter-varying systems in input-output representation",
abstract = "In this paper, we propose a method for model predictive control of linear parameter-varying (LPV) systems described in an input-output (IO) representation and subject to input- and output constraints. By assuming exact knowledge of the future trajectory of the scheduling variable, the on-line computations reduce to the solution of a nominal predictive control problem. An incremental non-minimal state-space representation is used as a prediction model, giving a controller with integral action suitable for tracking piecewise-constant reference signals. Closed-loop asymptotic stability is guaranteed by a terminal cost and terminal set constraint, and the computation of an ellipsoidal terminal set is discussed. Numerical examples demonstrate the properties of the proposed approach. When exact future knowledge of the scheduling variable is not available, we argue and show that good practical performance can be obtained by a scheduling prediction strategy.",
author = "J. Hanema and R. T{\'o}th and M. Lazar and H.S. Abbas",
year = "2016",
doi = "10.1109/ISIC.2016.7579987",
language = "English",
pages = "354--359",
booktitle = "Proceedings of the 2016 IEEE International Symposium on Intelligent Control (ISIC), 20-22 september 2016, Buenos Aires, Argentina",
publisher = "Institute of Electrical and Electronics Engineers",
address = "United States",

}

Hanema, J, Tóth, R, Lazar, M & Abbas, HS 2016, MPC for linear parameter-varying systems in input-output representation. in Proceedings of the 2016 IEEE International Symposium on Intelligent Control (ISIC), 20-22 september 2016, Buenos Aires, Argentina. Institute of Electrical and Electronics Engineers, Piscataway, pp. 354-359, 2016 IEEE International Symposium on Intelligent Control, Buenos Aires, Argentina, 20/09/16. DOI: 10.1109/ISIC.2016.7579987

MPC for linear parameter-varying systems in input-output representation. / Hanema, J.; Tóth, R.; Lazar, M.; Abbas, H.S.

Proceedings of the 2016 IEEE International Symposium on Intelligent Control (ISIC), 20-22 september 2016, Buenos Aires, Argentina. Piscataway : Institute of Electrical and Electronics Engineers, 2016. p. 354-359.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

TY - GEN

T1 - MPC for linear parameter-varying systems in input-output representation

AU - Hanema,J.

AU - Tóth,R.

AU - Lazar,M.

AU - Abbas,H.S.

PY - 2016

Y1 - 2016

N2 - In this paper, we propose a method for model predictive control of linear parameter-varying (LPV) systems described in an input-output (IO) representation and subject to input- and output constraints. By assuming exact knowledge of the future trajectory of the scheduling variable, the on-line computations reduce to the solution of a nominal predictive control problem. An incremental non-minimal state-space representation is used as a prediction model, giving a controller with integral action suitable for tracking piecewise-constant reference signals. Closed-loop asymptotic stability is guaranteed by a terminal cost and terminal set constraint, and the computation of an ellipsoidal terminal set is discussed. Numerical examples demonstrate the properties of the proposed approach. When exact future knowledge of the scheduling variable is not available, we argue and show that good practical performance can be obtained by a scheduling prediction strategy.

AB - In this paper, we propose a method for model predictive control of linear parameter-varying (LPV) systems described in an input-output (IO) representation and subject to input- and output constraints. By assuming exact knowledge of the future trajectory of the scheduling variable, the on-line computations reduce to the solution of a nominal predictive control problem. An incremental non-minimal state-space representation is used as a prediction model, giving a controller with integral action suitable for tracking piecewise-constant reference signals. Closed-loop asymptotic stability is guaranteed by a terminal cost and terminal set constraint, and the computation of an ellipsoidal terminal set is discussed. Numerical examples demonstrate the properties of the proposed approach. When exact future knowledge of the scheduling variable is not available, we argue and show that good practical performance can be obtained by a scheduling prediction strategy.

U2 - 10.1109/ISIC.2016.7579987

DO - 10.1109/ISIC.2016.7579987

M3 - Conference contribution

SP - 354

EP - 359

BT - Proceedings of the 2016 IEEE International Symposium on Intelligent Control (ISIC), 20-22 september 2016, Buenos Aires, Argentina

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

ER -

Hanema J, Tóth R, Lazar M, Abbas HS. MPC for linear parameter-varying systems in input-output representation. In Proceedings of the 2016 IEEE International Symposium on Intelligent Control (ISIC), 20-22 september 2016, Buenos Aires, Argentina. Piscataway: Institute of Electrical and Electronics Engineers. 2016. p. 354-359. Available from, DOI: 10.1109/ISIC.2016.7579987