Reduced order modelling and predictive control of multivariable nonlinear process

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F18%3A39913530" target="_blank" >RIV/00216275:25530/18:39913530 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s12046-018-0798-x" target="_blank" >http://dx.doi.org/10.1007/s12046-018-0798-x</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s12046-018-0798-x" target="_blank" >10.1007/s12046-018-0798-x</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Reduced order modelling and predictive control of multivariable nonlinear process

Popis výsledku v původním jazyce

In this paper, an efficient model-predictive control strategy that can be applied to complex multivariable process is presented. A reduced order generalized predictive algorithm is proposed for online applications with reduction in complexity and time elapsed. The complex multivariable process considered in this work is a binary distillation column. The reduced order model is developed with a recently proposed hybrid algorithm known as Clustering Dominant Pole Algorithm and is able to compute the full set of dominant poles and their cluster centre efficiently. The controller calculates the optimal control action based on the future reference signals, current state and constraints on manipulated and controlled variables for a high-order dynamic simulated model of nonlinear multivariable binary distillation column process. The predictive control algorithm uses controlled auto-regressive integrated moving average model. The performance of constraint generalized predictive control scheme is found to be superior to that of the conventional PID controller in terms of overshoot, settling time and performance indices, mainly ISE, IAE and MSE.

Název v anglickém jazyce

Reduced order modelling and predictive control of multivariable nonlinear process

Popis výsledku anglicky

In this paper, an efficient model-predictive control strategy that can be applied to complex multivariable process is presented. A reduced order generalized predictive algorithm is proposed for online applications with reduction in complexity and time elapsed. The complex multivariable process considered in this work is a binary distillation column. The reduced order model is developed with a recently proposed hybrid algorithm known as Clustering Dominant Pole Algorithm and is able to compute the full set of dominant poles and their cluster centre efficiently. The controller calculates the optimal control action based on the future reference signals, current state and constraints on manipulated and controlled variables for a high-order dynamic simulated model of nonlinear multivariable binary distillation column process. The predictive control algorithm uses controlled auto-regressive integrated moving average model. The performance of constraint generalized predictive control scheme is found to be superior to that of the conventional PID controller in terms of overshoot, settling time and performance indices, mainly ISE, IAE and MSE.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20205 - Automation and control systems

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Sadhana-Academy proceedings in engineering sciences

ISSN

0256-2499

e-ISSN

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Svazek periodika

43

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

IN - Indická republika

Počet stran výsledku

18

Strana od-do

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Kód UT WoS článku

000429178800005

EID výsledku v databázi Scopus

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