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

Kód výsledku v 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>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 2011 Australian Control Conference

ISBN

978-0-85825-987-4

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

422-427

Název nakladatele

Engineers Australia

Místo vydání

Brisbane

Místo konání akce

Melbourne

Datum konání akce

10. 11. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—