Finite Horizon MPC for Systems in Innovation Form

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Abstract

System identification and model predictive control have largely developed as two separate disciplines. Nevertheless, the major part of industrial MPC commissioning is generation of data and identification of models. In this contribution we attempt to bridge this gap by contributing some of the missing links. Input-output models (FIR, ARX, ARMAX, Box-Jenkins) as well as subspace models can be represented as state space models in innovation form. These models have correlated process and measurement noise. The correct LQG control law for systems with correlated process and measurement noise is not well known. We provide the correct finite-horizon LQG controller for this system and use this to develop a state space representation of the closed-loop system. This representation is used for closed-loop frequency and covariance analysis. These measures are used in tuning of the unconstrained and constrained MPC. We demonstrate our results on a simulated industrial furnace.
Original languageEnglish
Title of host publicationProceeding of the 50th IEEE Conference on Decision and Control and European Control Conference : IEEE CDC-ECC 2011
PublisherIEEE
Publication date2011
Pages1896-1903
ISBN (Print)978-1-61284-799-3
DOIs
Publication statusPublished - 2011
Event50th IEEE Conference on Decision and Control and European Control Conference - Hilton Orlando Bonnet Creek, Orlando, Florida, United States
Duration: 12 Dec 201115 Dec 2011
Conference number: 50
http://control.disp.uniroma2.it/cdcecc2011/

Conference

Conference50th IEEE Conference on Decision and Control and European Control Conference
Number50
LocationHilton Orlando Bonnet Creek
CountryUnited States
CityOrlando, Florida
Period12/12/201115/12/2011
SponsorSociety for Industrial and Applied Mathematics
Internet address

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