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

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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.
Original 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
Publication statusPublished - 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

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