Fuzzy modeling of a piezoelectric actuator

Morteza Mohammadzaheri, Steven Grainger, Mohsen Bazghaleh

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

In this research, a piezoelectric actuator was modeled using fuzzy subtractive clustering and neuro-fuzzy networks. In the literature, the use of various modeling techniques (excluding techniques used in this article) and different arrangements of inputs in black box modeling of piezoelectric actuators for the purpose of displacement prediction has been reported. Nowadays, universal approximators are available with proven ability in system modeling; hence, the modeling technique is no longer such a critical issue. Appropriate selection of the inputs to the model is, however, still an unsolved problem, with an absence of comparative studies. While the extremum values of input voltage and/or displacement in each cycle of operation have been used in black box modeling inspired by classical phenomenological methods, some researchers have ignored them. This article focuses on addressing this matter. Despite the fact that classical artificial neural networks, the most popular black box modeling tools, provide no visibility of the internal operation, neuro-fuzzy networks can be converted to fuzzy models. Fuzzy models comprise of fuzzy rules which are formed by a number of fuzzy or linguistic values, and this lets the researcher understand the role of each input in the model in comparison with other inputs, particularly, if fuzzy values (sets) have been selected through subtractive clustering. This unique advantage was employed in this research together with consideration of a few critical but subtle points in model verification which are usually overlooked in black box modeling of piezoelectric actuators.

Original languageEnglish
Pages (from-to)663-670
Number of pages8
JournalInternational Journal of Precision Engineering and Manufacturing
Volume13
Issue number5
DOIs
Publication statusPublished - May 2012

Fingerprint

Piezoelectric actuators
Fuzzy clustering
Fuzzy rules
Linguistics
Visibility
Neural networks
Electric potential

Keywords

  • ANFIS
  • Black box modeling
  • Fuzzy
  • Input and output extrema
  • Input arrangement
  • Piezoelectric actuator

ASJC Scopus subject areas

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Cite this

Fuzzy modeling of a piezoelectric actuator. / Mohammadzaheri, Morteza; Grainger, Steven; Bazghaleh, Mohsen.

In: International Journal of Precision Engineering and Manufacturing, Vol. 13, No. 5, 05.2012, p. 663-670.

Research output: Contribution to journalArticle

Mohammadzaheri, Morteza ; Grainger, Steven ; Bazghaleh, Mohsen. / Fuzzy modeling of a piezoelectric actuator. In: International Journal of Precision Engineering and Manufacturing. 2012 ; Vol. 13, No. 5. pp. 663-670.
@article{39bc150a62804d21b5bbdb787a15eb61,
title = "Fuzzy modeling of a piezoelectric actuator",
abstract = "In this research, a piezoelectric actuator was modeled using fuzzy subtractive clustering and neuro-fuzzy networks. In the literature, the use of various modeling techniques (excluding techniques used in this article) and different arrangements of inputs in black box modeling of piezoelectric actuators for the purpose of displacement prediction has been reported. Nowadays, universal approximators are available with proven ability in system modeling; hence, the modeling technique is no longer such a critical issue. Appropriate selection of the inputs to the model is, however, still an unsolved problem, with an absence of comparative studies. While the extremum values of input voltage and/or displacement in each cycle of operation have been used in black box modeling inspired by classical phenomenological methods, some researchers have ignored them. This article focuses on addressing this matter. Despite the fact that classical artificial neural networks, the most popular black box modeling tools, provide no visibility of the internal operation, neuro-fuzzy networks can be converted to fuzzy models. Fuzzy models comprise of fuzzy rules which are formed by a number of fuzzy or linguistic values, and this lets the researcher understand the role of each input in the model in comparison with other inputs, particularly, if fuzzy values (sets) have been selected through subtractive clustering. This unique advantage was employed in this research together with consideration of a few critical but subtle points in model verification which are usually overlooked in black box modeling of piezoelectric actuators.",
keywords = "ANFIS, Black box modeling, Fuzzy, Input and output extrema, Input arrangement, Piezoelectric actuator",
author = "Morteza Mohammadzaheri and Steven Grainger and Mohsen Bazghaleh",
year = "2012",
month = "5",
doi = "10.1007/s12541-012-0086-3",
language = "English",
volume = "13",
pages = "663--670",
journal = "International Journal of Precision Engineering and Manufacturing",
issn = "2234-7593",
publisher = "Korean Society of Precision Engineering",
number = "5",

}

TY - JOUR

T1 - Fuzzy modeling of a piezoelectric actuator

AU - Mohammadzaheri, Morteza

AU - Grainger, Steven

AU - Bazghaleh, Mohsen

PY - 2012/5

Y1 - 2012/5

N2 - In this research, a piezoelectric actuator was modeled using fuzzy subtractive clustering and neuro-fuzzy networks. In the literature, the use of various modeling techniques (excluding techniques used in this article) and different arrangements of inputs in black box modeling of piezoelectric actuators for the purpose of displacement prediction has been reported. Nowadays, universal approximators are available with proven ability in system modeling; hence, the modeling technique is no longer such a critical issue. Appropriate selection of the inputs to the model is, however, still an unsolved problem, with an absence of comparative studies. While the extremum values of input voltage and/or displacement in each cycle of operation have been used in black box modeling inspired by classical phenomenological methods, some researchers have ignored them. This article focuses on addressing this matter. Despite the fact that classical artificial neural networks, the most popular black box modeling tools, provide no visibility of the internal operation, neuro-fuzzy networks can be converted to fuzzy models. Fuzzy models comprise of fuzzy rules which are formed by a number of fuzzy or linguistic values, and this lets the researcher understand the role of each input in the model in comparison with other inputs, particularly, if fuzzy values (sets) have been selected through subtractive clustering. This unique advantage was employed in this research together with consideration of a few critical but subtle points in model verification which are usually overlooked in black box modeling of piezoelectric actuators.

AB - In this research, a piezoelectric actuator was modeled using fuzzy subtractive clustering and neuro-fuzzy networks. In the literature, the use of various modeling techniques (excluding techniques used in this article) and different arrangements of inputs in black box modeling of piezoelectric actuators for the purpose of displacement prediction has been reported. Nowadays, universal approximators are available with proven ability in system modeling; hence, the modeling technique is no longer such a critical issue. Appropriate selection of the inputs to the model is, however, still an unsolved problem, with an absence of comparative studies. While the extremum values of input voltage and/or displacement in each cycle of operation have been used in black box modeling inspired by classical phenomenological methods, some researchers have ignored them. This article focuses on addressing this matter. Despite the fact that classical artificial neural networks, the most popular black box modeling tools, provide no visibility of the internal operation, neuro-fuzzy networks can be converted to fuzzy models. Fuzzy models comprise of fuzzy rules which are formed by a number of fuzzy or linguistic values, and this lets the researcher understand the role of each input in the model in comparison with other inputs, particularly, if fuzzy values (sets) have been selected through subtractive clustering. This unique advantage was employed in this research together with consideration of a few critical but subtle points in model verification which are usually overlooked in black box modeling of piezoelectric actuators.

KW - ANFIS

KW - Black box modeling

KW - Fuzzy

KW - Input and output extrema

KW - Input arrangement

KW - Piezoelectric actuator

UR - http://www.scopus.com/inward/record.url?scp=84861875016&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84861875016&partnerID=8YFLogxK

U2 - 10.1007/s12541-012-0086-3

DO - 10.1007/s12541-012-0086-3

M3 - Article

VL - 13

SP - 663

EP - 670

JO - International Journal of Precision Engineering and Manufacturing

JF - International Journal of Precision Engineering and Manufacturing

SN - 2234-7593

IS - 5

ER -