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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.
| Original language | English |
|---|---|
| Title of host publication | Control-Oriented Modelling and Identification: Theory and Practice |
| Publisher | Institution of Engineering and Technology |
| Pages | 125-142 |
| Number of pages | 18 |
| ISBN (Electronic) | 9781849196154 |
| ISBN (Print) | 9781849196147 |
| DOIs | |
| Publication status | Published - 2015 Jan 1 |
Subject classification (UKÄ)
- Control Engineering
Free 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
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Dive into the research topics of 'Subspace-Based Multi-Step Predictors for Predictive Control'. Together they form a unique fingerprint.Projects
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DIAdvisor
Ståhl, F. (Researcher), Rönn, M. (Researcher), Cescon, M. (Researcher) & Johansson, R. (PI)
2008/03/01 → 2012/02/29
Project: Research