Enhancing the generality of fuzzy relational models for control

Bhooshan Kelkar, Bruce Postlethwaite

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

A promising area of research in fuzzy control is the model-based fuzzy controller. At the heart of this approach is a fuzzy relational model of the process to be controlled. Since this model is identified directly from process input-output data it is likely that 'holes' will be present in the identified relational model. These holes pose real problems when the model is incorporated into a model-based controller since the model will be unable to make any predictions whatsoever if the system drifts into an unknown region. The present work deals with the completeness of the fuzzy relational model which forms the core of the controller. This work proposes a scheme of post-processing to 'fill in' the fuzzy relational model once it has been built and thereby improve its applicability for on-line control. A comparative study of the post-processed model and conventional relational model is presented for Box-Jenkins data identification system and a real-time, highly non-linear application of pH control identification.
LanguageEnglish
Pages117-129
Number of pages12
JournalFuzzy Sets and Systems
Volume100
Issue number1-3
DOIs
Publication statusPublished - 16 Nov 1998

Fingerprint

Relational Model
Fuzzy Model
Model-based
Controller
Fuzzy Controller
Fuzzy Control
System Identification
Post-processing
Model
Comparative Study
Completeness
Controllers
Likely
Real-time
Unknown
Prediction
Output
Fuzzy control
Identification (control systems)

Keywords

  • fuzzy modelling control
  • fuzzy control
  • relational modelling
  • model-based control

Cite this

Kelkar, Bhooshan ; Postlethwaite, Bruce. / Enhancing the generality of fuzzy relational models for control. In: Fuzzy Sets and Systems. 1998 ; Vol. 100, No. 1-3. pp. 117-129.
@article{d2de792bb7b7490ab15ec75a1a8506ab,
title = "Enhancing the generality of fuzzy relational models for control",
abstract = "A promising area of research in fuzzy control is the model-based fuzzy controller. At the heart of this approach is a fuzzy relational model of the process to be controlled. Since this model is identified directly from process input-output data it is likely that 'holes' will be present in the identified relational model. These holes pose real problems when the model is incorporated into a model-based controller since the model will be unable to make any predictions whatsoever if the system drifts into an unknown region. The present work deals with the completeness of the fuzzy relational model which forms the core of the controller. This work proposes a scheme of post-processing to 'fill in' the fuzzy relational model once it has been built and thereby improve its applicability for on-line control. A comparative study of the post-processed model and conventional relational model is presented for Box-Jenkins data identification system and a real-time, highly non-linear application of pH control identification.",
keywords = "fuzzy modelling control, fuzzy control, relational modelling, model-based control",
author = "Bhooshan Kelkar and Bruce Postlethwaite",
year = "1998",
month = "11",
day = "16",
doi = "10.1016/S0165-0114(97)00045-6",
language = "English",
volume = "100",
pages = "117--129",
journal = "Fuzzy Sets and Systems",
issn = "0165-0114",
number = "1-3",

}

Enhancing the generality of fuzzy relational models for control. / Kelkar, Bhooshan; Postlethwaite, Bruce.

In: Fuzzy Sets and Systems, Vol. 100, No. 1-3, 16.11.1998, p. 117-129.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Enhancing the generality of fuzzy relational models for control

AU - Kelkar, Bhooshan

AU - Postlethwaite, Bruce

PY - 1998/11/16

Y1 - 1998/11/16

N2 - A promising area of research in fuzzy control is the model-based fuzzy controller. At the heart of this approach is a fuzzy relational model of the process to be controlled. Since this model is identified directly from process input-output data it is likely that 'holes' will be present in the identified relational model. These holes pose real problems when the model is incorporated into a model-based controller since the model will be unable to make any predictions whatsoever if the system drifts into an unknown region. The present work deals with the completeness of the fuzzy relational model which forms the core of the controller. This work proposes a scheme of post-processing to 'fill in' the fuzzy relational model once it has been built and thereby improve its applicability for on-line control. A comparative study of the post-processed model and conventional relational model is presented for Box-Jenkins data identification system and a real-time, highly non-linear application of pH control identification.

AB - A promising area of research in fuzzy control is the model-based fuzzy controller. At the heart of this approach is a fuzzy relational model of the process to be controlled. Since this model is identified directly from process input-output data it is likely that 'holes' will be present in the identified relational model. These holes pose real problems when the model is incorporated into a model-based controller since the model will be unable to make any predictions whatsoever if the system drifts into an unknown region. The present work deals with the completeness of the fuzzy relational model which forms the core of the controller. This work proposes a scheme of post-processing to 'fill in' the fuzzy relational model once it has been built and thereby improve its applicability for on-line control. A comparative study of the post-processed model and conventional relational model is presented for Box-Jenkins data identification system and a real-time, highly non-linear application of pH control identification.

KW - fuzzy modelling control

KW - fuzzy control

KW - relational modelling

KW - model-based control

UR - http://dx.doi.org/10.1016/S0165-0114(97)00045-6

U2 - 10.1016/S0165-0114(97)00045-6

DO - 10.1016/S0165-0114(97)00045-6

M3 - Article

VL - 100

SP - 117

EP - 129

JO - Fuzzy Sets and Systems

T2 - Fuzzy Sets and Systems

JF - Fuzzy Sets and Systems

SN - 0165-0114

IS - 1-3

ER -