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Statements

Subject Item
n2:RIV%2F00216275%3A25530%2F07%3A00005225%21RIV08-MSM-25530___
rdf:type
n12:Vysledek skos:Concept
dcterms:description
Model Predictive Control je obecná problematika jak navrhovat řízení v diskrétní oblasti. Integrated Controller Auto-Regressive Moving-Average (CARIMA) model řízeného systému je použit pro výpočet predikce budoucího chování soustavy. Model šumu - polynomiální matici lze chápat jako filtr dat, který snižuje citlivost regulátoru k poruchám o vysokých frekvencích. Otázkou zůstává jak volit nebo navrhnout tento filtr. Je navržen Kalmánův pozorovatel pro daný proces a parametry šumu a jeho charakteristický polynom je použit při výpočtu regulátoru. Navrhovaná metoda je demonstrována na řízení laboratorní úlohy. Model Predictive Control is general methodology how to cope with optimal control problem in discrete-time domain. Integrated Controller Auto-Regressive Moving-Average (CARIMA) process model is used for future plant output predictions by Generalized Predictive Controller. Noise polynomial matrix in process model serves as a data filter to detune controller sensitivity to high frequency disturbances e.g. against measurement noise. Open question remains how to design this filter. In the paper steady-state Kalman estimator is calculated for given process model and noise parameters. Its characteristic polynomial is used as a process model noise polynomial ? that means Kalman estimator as a process model is controlled. Suggested method is proved on laboratory plant control experiments. Model Predictive Control is general methodology how to cope with optimal control problem in discrete-time domain. Integrated Controller Auto-Regressive Moving-Average (CARIMA) process model is used for future plant output predictions by Generalized Predictive Controller. Noise polynomial matrix in process model serves as a data filter to detune controller sensitivity to high frequency disturbances e.g. against measurement noise. Open question remains how to design this filter. In the paper steady-state Kalman estimator is calculated for given process model and noise parameters. Its characteristic polynomial is used as a process model noise polynomial ? that means Kalman estimator as a process model is controlled. Suggested method is proved on laboratory plant control experiments.
dcterms:title
Predictive control based on CARIMA process model and Kalman estimator Predictive control based on CARIMA process model and Kalman estimator Prediktivní řízení založené na CARIMA modelu a Kalmánově pozorovateli
skos:prefLabel
Prediktivní řízení založené na CARIMA modelu a Kalmánově pozorovateli Predictive control based on CARIMA process model and Kalman estimator Predictive control based on CARIMA process model and Kalman estimator
skos:notation
RIV/00216275:25530/07:00005225!RIV08-MSM-25530___
n3:strany
39-42
n3:aktivita
n16:Z
n3:aktivity
Z(MSM0021627505)
n3:cisloPeriodika
IX
n3:dodaniDat
n10:2008
n3:domaciTvurceVysledku
n14:4112245
n3:druhVysledku
n11:J
n3:duvernostUdaju
n4:S
n3:entitaPredkladatele
n6:predkladatel
n3:idSjednocenehoVysledku
443571
n3:idVysledku
RIV/00216275:25530/07:00005225
n3:jazykVysledku
n9:eng
n3:klicovaSlova
Generalized Predictive Control; Kalman estimator; T-polynomial matrix
n3:klicoveSlovo
n15:Kalman%20estimator n15:Generalized%20Predictive%20Control n15:T-polynomial%20matrix
n3:kodStatuVydavatele
RO - Rumunsko
n3:kontrolniKodProRIV
[A3C5673D884F]
n3:nazevZdroje
Ovidius University Annual Scientific Journal - Mechanical engineering series
n3:obor
n13:BC
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:rokUplatneniVysledku
n10:2007
n3:svazekPeriodika
1
n3:tvurceVysledku
Honc, Daniel
n3:zamer
n8:MSM0021627505
s:issn
1224-1776
s:numberOfPages
4
n18:organizacniJednotka
25530