Model based control using fuzzy relational models

B. A. Gormandy; B. E. Postlethwaite

doi:10.1109/FUZZ.2001.1008966

Model based control using fuzzy relational models

B. A. Gormandy^*, B. E. Postlethwaite

^*Corresponding author for this work

Chemical And Process Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

2 Citations (Scopus)

Abstract

The development of fuzzy internal model control (FIMC) is analogous to that of linear IMC. At the core of the FIMC is the novel crisp consequent fuzzy relational model (ccFRM). In this paper, we compare the behaviour of FIMC to IMC on a variety of linear systems. The comparison shows that, although FIMC behaves well when used with SISO systems, it suffers severe difficulties in dealing with MIMO systems with dead-times. A possible solution to this, again from analogy with linear IMC, is to approximate the model inverse with a model predictive controller.

Original language	English
Title of host publication	10th IEEE International Conference on Fuzzy Systems, 2001
Publisher	IEEE
Pages	1581-1584
Number of pages	4
Volume	3
ISBN (Print)	078037293X
DOIs	https://doi.org/10.1109/FUZZ.2001.1008966
Publication status	Published - Dec 2001
Event	10th IEEE International Conference on Fuzzy Systems - Melbourne, United Kingdom Duration: 2 Dec 2001 → 5 Dec 2001

Conference

Conference	10th IEEE International Conference on Fuzzy Systems
Country/Territory	United Kingdom
City	Melbourne
Period	2/12/01 → 5/12/01

Keywords

MIMO systems
SISO systems
crisp consequent fuzzy relational model
fuzzy internal model control
linear systems
model based control
model predictive controller

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10.1109/FUZZ.2001.1008966

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@inproceedings{939346022419497496c9d76bbbf0245d,

title = "Model based control using fuzzy relational models",

abstract = "The development of fuzzy internal model control (FIMC) is analogous to that of linear IMC. At the core of the FIMC is the novel crisp consequent fuzzy relational model (ccFRM). In this paper, we compare the behaviour of FIMC to IMC on a variety of linear systems. The comparison shows that, although FIMC behaves well when used with SISO systems, it suffers severe difficulties in dealing with MIMO systems with dead-times. A possible solution to this, again from analogy with linear IMC, is to approximate the model inverse with a model predictive controller.",

keywords = "MIMO systems, SISO systems, crisp consequent fuzzy relational model, fuzzy internal model control, linear systems, model based control, model predictive controller",

author = "Gormandy, {B. A.} and Postlethwaite, {B. E.}",

year = "2001",

month = dec,

doi = "10.1109/FUZZ.2001.1008966",

language = "English",

isbn = "078037293X",

volume = "3",

pages = "1581--1584",

booktitle = "10th IEEE International Conference on Fuzzy Systems, 2001",

publisher = "IEEE",

note = "10th IEEE International Conference on Fuzzy Systems ; Conference date: 02-12-2001 Through 05-12-2001",

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TY - GEN

T1 - Model based control using fuzzy relational models

AU - Gormandy, B. A.

AU - Postlethwaite, B. E.

PY - 2001/12

Y1 - 2001/12

N2 - The development of fuzzy internal model control (FIMC) is analogous to that of linear IMC. At the core of the FIMC is the novel crisp consequent fuzzy relational model (ccFRM). In this paper, we compare the behaviour of FIMC to IMC on a variety of linear systems. The comparison shows that, although FIMC behaves well when used with SISO systems, it suffers severe difficulties in dealing with MIMO systems with dead-times. A possible solution to this, again from analogy with linear IMC, is to approximate the model inverse with a model predictive controller.

AB - The development of fuzzy internal model control (FIMC) is analogous to that of linear IMC. At the core of the FIMC is the novel crisp consequent fuzzy relational model (ccFRM). In this paper, we compare the behaviour of FIMC to IMC on a variety of linear systems. The comparison shows that, although FIMC behaves well when used with SISO systems, it suffers severe difficulties in dealing with MIMO systems with dead-times. A possible solution to this, again from analogy with linear IMC, is to approximate the model inverse with a model predictive controller.

KW - MIMO systems

KW - SISO systems

KW - crisp consequent fuzzy relational model

KW - fuzzy internal model control

KW - linear systems

KW - model based control

KW - model predictive controller

UR - http://www.scopus.com/inward/record.url?scp=0036343532&partnerID=8YFLogxK

U2 - 10.1109/FUZZ.2001.1008966

DO - 10.1109/FUZZ.2001.1008966

M3 - Conference contribution book

SN - 078037293X

VL - 3

SP - 1581

EP - 1584

BT - 10th IEEE International Conference on Fuzzy Systems, 2001

PB - IEEE

T2 - 10th IEEE International Conference on Fuzzy Systems

Y2 - 2 December 2001 through 5 December 2001

ER -

Model based control using fuzzy relational models

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Keywords

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