Achieving State Estimation Equivalence for Misassigned Disturbances in Offset-Free Model Predictive Control

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)396-407
Journal / PublicationAICHE Journal
Volume55
Issue number2
Online published6 Jan 2009
Publication statusPublished - Feb 2009
Externally publishedYes

Abstract

Integrated white noise disturbance models are included in advanced control strategies, such as Model Predictive Control, to remove offset when there are unmodeled disturbances or plant/model mismatch. These integrating disturbances are usually modeled to enter either through the plant inputs or the plant outputs or partially through both. There is currently a lack of consensus in the literature on the best choice for the structure of this disturbance model to obtain good feedback control. We show that the choice of the disturbance model does not affect the closed- loop performance if appropriate covariances are used in specifying the state estimator. We also present a data based autocovariance technique to estimate the appropriate covariances regardless of the plant's true unknown disturbance source. The covariances estimated using the autocovariance technique and the resulting estimator gain are shown to compensate for an incorrect choice of the source of the disturbance in the disturbance model. © 2009 American Institute of Chemical Engineers.

Research Area(s)

  • Disturbance models, Model equivalence, Model predictive control, State estimation

Citation Format(s)

Achieving State Estimation Equivalence for Misassigned Disturbances in Offset-Free Model Predictive Control. / Rajamani, Murali R.; Rawlings, James B.; Qin, S. Joe.
In: AICHE Journal, Vol. 55, No. 2, 02.2009, p. 396-407.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review