Model Predictive Control Relevant Identification Using Partial Least Squares for Building Modeling

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F11%3A00184058" target="_blank" >RIV/68407700:21230/11:00184058 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Model Predictive Control Relevant Identification Using Partial Least Squares for Building Modeling
Original language description
A dynamic model is a crucial part when dealing with the Model Predictive Controller (MPC). Very often a Prediction Error Method (PEM) framework is chosen for identification/ modeling. In this framework one-step ahead prediction errors are minimized. In contrast, we examine a multi-step ahead prediction error cost function which satisfies the needs of the MPC for having a good prediction on a prediction horizon. Least Squares (LS) are probably the most often used tool in a variety of optimization algorithms. In case of ill-conditioned data due to e.g. co-linearity, the Ordinary LS (OLS) fail and provide biased estimate. We adapt an approach known in econometric, chemistry and biometrics, namely Partial Least Squares (PLS) and introduce an algorithm which combines the PLS and multi-step ahead algorithm. The proposed algorithm is then tested and verified.
Czech name
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Czech description
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Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 2011 Australian Control Conference
ISBN
978-0-85825-987-4
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
422-427
Publisher name
Engineers Australia
Place of publication
Brisbane
Event location
Melbourne
Event date
Nov 10, 2011
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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