Adaptive predictive control using multiple models, switching and tuning

Leonardo Giovanini, Andrzej Ordys, Michael Grimble

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

In this work, a new method of design adaptive controllers for SISO systems based on multiple models and switching is presented. The controller selects the model from a given set, according to a switching rule based on output prediction errors. The goal is to design, at each sample instant, a predictive control law that ensures the robust stability of the closed-loop system and achieves the best performance for the current operating point. At each sample the proposed control scheme identifies a set of linear models that best characterizes the dynamics of the current operating region. Then, it carries out an automatic reconfiguration of the controller to achieve the best possible performance whilst providing a guarantee of robust closed-loop stability. The results are illustrated by simulations a nonlinear continuous and stirred tank reactor.
LanguageEnglish
Pages669-681
Number of pages12
JournalInternational Journal of Control, Automation and Systems
Volume4
Issue number6
Publication statusPublished - 2006

Fingerprint

Tuning
Controllers
Closed loop systems
Robust stability

Keywords

  • adaptive control
  • infinite controller cover set
  • multiple models
  • multi-objective optimization
  • predictive control

Cite this

@article{f99d8d6cbae24f8ba313c43dcf49affb,
title = "Adaptive predictive control using multiple models, switching and tuning",
abstract = "In this work, a new method of design adaptive controllers for SISO systems based on multiple models and switching is presented. The controller selects the model from a given set, according to a switching rule based on output prediction errors. The goal is to design, at each sample instant, a predictive control law that ensures the robust stability of the closed-loop system and achieves the best performance for the current operating point. At each sample the proposed control scheme identifies a set of linear models that best characterizes the dynamics of the current operating region. Then, it carries out an automatic reconfiguration of the controller to achieve the best possible performance whilst providing a guarantee of robust closed-loop stability. The results are illustrated by simulations a nonlinear continuous and stirred tank reactor.",
keywords = "adaptive control, infinite controller cover set, multiple models, multi-objective optimization, predictive control",
author = "Leonardo Giovanini and Andrzej Ordys and Michael Grimble",
year = "2006",
language = "English",
volume = "4",
pages = "669--681",
journal = "International Journal of Control, Automation and Systems",
issn = "1598-6446",
number = "6",

}

Adaptive predictive control using multiple models, switching and tuning. / Giovanini, Leonardo; Ordys, Andrzej; Grimble, Michael.

In: International Journal of Control, Automation and Systems, Vol. 4, No. 6, 2006, p. 669-681.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Adaptive predictive control using multiple models, switching and tuning

AU - Giovanini, Leonardo

AU - Ordys, Andrzej

AU - Grimble, Michael

PY - 2006

Y1 - 2006

N2 - In this work, a new method of design adaptive controllers for SISO systems based on multiple models and switching is presented. The controller selects the model from a given set, according to a switching rule based on output prediction errors. The goal is to design, at each sample instant, a predictive control law that ensures the robust stability of the closed-loop system and achieves the best performance for the current operating point. At each sample the proposed control scheme identifies a set of linear models that best characterizes the dynamics of the current operating region. Then, it carries out an automatic reconfiguration of the controller to achieve the best possible performance whilst providing a guarantee of robust closed-loop stability. The results are illustrated by simulations a nonlinear continuous and stirred tank reactor.

AB - In this work, a new method of design adaptive controllers for SISO systems based on multiple models and switching is presented. The controller selects the model from a given set, according to a switching rule based on output prediction errors. The goal is to design, at each sample instant, a predictive control law that ensures the robust stability of the closed-loop system and achieves the best performance for the current operating point. At each sample the proposed control scheme identifies a set of linear models that best characterizes the dynamics of the current operating region. Then, it carries out an automatic reconfiguration of the controller to achieve the best possible performance whilst providing a guarantee of robust closed-loop stability. The results are illustrated by simulations a nonlinear continuous and stirred tank reactor.

KW - adaptive control

KW - infinite controller cover set

KW - multiple models

KW - multi-objective optimization

KW - predictive control

UR - http://www.ijcas.com/admin/paper/files/IJCAS_v4_n6_pp.669-681.pdf

M3 - Article

VL - 4

SP - 669

EP - 681

JO - International Journal of Control, Automation and Systems

T2 - International Journal of Control, Automation and Systems

JF - International Journal of Control, Automation and Systems

SN - 1598-6446

IS - 6

ER -