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Subspace-based multi-step predictors for predictive control

Cescon, Marzia LU and Johansson, Rolf LU (2015) In Control-Oriented Modelling and Identification: Theory and Practice p.125-142
Abstract

In the framework of the subspace-based identification of linear systems, the first step for the construction of a state-space model from observed input-output data involves the estimation of the output predictor. Such construction is based on projection operations of certain structured data matrices onto suitable subspaces spanned by the collected data. To the purpose of predictive control using short-term predictors, this algorithmic step can be elaborated to provide data-based multi-step predictors. Using such an approach, this contribution deals with subspace-based identification methods for the estimation of short-term predictors. One illustrative example is provided: blood glucose prediction in type 1 diabetes mellitus.

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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Blood glucose prediction, Data-based multistep predictors, Identification, Linear systems, Matrix algebra, Output predictor estimation, Predictive control, Projection operations, Short-term predictor estimation, Short-term predictors, State-space model, Structured data matrices, Subspace-based identification, Subspace-based multistep predictors, Type 1 diabetes mellitus
in
Control-Oriented Modelling and Identification: Theory and Practice
pages
18 pages
publisher
Institution of Engineering and Technology
external identifiers
  • scopus:85011784985
ISBN
9781849196154
9781849196147
DOI
10.1049/PBCE080E_ch6
language
English
LU publication?
yes
id
cc44870c-ff2a-4740-9d9c-5054bf8f4301
date added to LUP
2017-02-23 13:44:42
date last changed
2017-06-08 11:23:40
@inbook{cc44870c-ff2a-4740-9d9c-5054bf8f4301,
  abstract     = {<p>In the framework of the subspace-based identification of linear systems, the first step for the construction of a state-space model from observed input-output data involves the estimation of the output predictor. Such construction is based on projection operations of certain structured data matrices onto suitable subspaces spanned by the collected data. To the purpose of predictive control using short-term predictors, this algorithmic step can be elaborated to provide data-based multi-step predictors. Using such an approach, this contribution deals with subspace-based identification methods for the estimation of short-term predictors. One illustrative example is provided: blood glucose prediction in type 1 diabetes mellitus.</p>},
  author       = {Cescon, Marzia and Johansson, Rolf},
  isbn         = {9781849196154},
  keyword      = {Blood glucose prediction,Data-based multistep predictors,Identification,Linear systems,Matrix algebra,Output predictor estimation,Predictive control,Projection operations,Short-term predictor estimation,Short-term predictors,State-space model,Structured data matrices,Subspace-based identification,Subspace-based multistep predictors,Type 1 diabetes mellitus},
  language     = {eng},
  month        = {01},
  pages        = {125--142},
  publisher    = {Institution of Engineering and Technology},
  series       = {Control-Oriented Modelling and Identification: Theory and Practice},
  title        = {Subspace-based multi-step predictors for predictive control},
  url          = {http://dx.doi.org/10.1049/PBCE080E_ch6},
  year         = {2015},
}