Subspace-Based Multi-Step Predictors for Predictive Control
(2015) 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/cc44870c-ff2a-4740-9d9c-5054bf8f4301
- author
- Cescon, Marzia LU and Johansson, Rolf LU
- organization
- publishing date
- 2015-01-01
- 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
- host publication
- 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
- project
- DIAdvisor
- language
- English
- LU publication?
- yes
- id
- cc44870c-ff2a-4740-9d9c-5054bf8f4301
- date added to LUP
- 2017-02-23 13:44:42
- date last changed
- 2024-01-13 15:31:36
@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}}, booktitle = {{Control-Oriented Modelling and Identification: Theory and Practice}}, isbn = {{9781849196154}}, 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}}, language = {{eng}}, month = {{01}}, pages = {{125--142}}, publisher = {{Institution of Engineering and Technology}}, title = {{Subspace-Based Multi-Step Predictors for Predictive Control}}, url = {{http://dx.doi.org/10.1049/PBCE080E_ch6}}, doi = {{10.1049/PBCE080E_ch6}}, year = {{2015}}, }