Mean-based fuzzy identifier and control of uncertain nonlinear systems

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

A mean-based adaptive fuzzy control scheme with state estimation performance is proposed for a class of uncertain nonlinear systems in the presence of only output measurement. In the control scheme, a mean-based fuzzy identifier without prior knowledge of membership functions is merged into direct adaptive controller with a linear state estimator. The structure of the mean-based fuzzy identifier is nonlinear in the adjusted parameters in order to diminish the unfavorable influence of initially designing membership functions on control performance. For the nonlinear structure, a mean method is used to derive adaptive laws. Compared with conventional methods, the advantage of the mean method is that the computation burden can be effectively alleviated because finding the derivative of fuzzy systems is not required. In addition, for the linear state estimator, the state estimation performance with beforehand given attenuation parameter is established by the design of a compensative controller. Finally, two examples are provided to demonstrate the applicability of the proposed scheme.

Original languageEnglish
Pages (from-to)837-858
Number of pages22
JournalFuzzy Sets and Systems
Volume161
Issue number6
DOIs
Publication statusPublished - 2010 Mar 16

Fingerprint

Uncertain Nonlinear Systems
State estimation
Membership functions
Nonlinear systems
Controllers
Fuzzy systems
Fuzzy control
State Estimation
Membership Function
Derivatives
Controller
Estimator
Adaptive Fuzzy Control
Prior Knowledge
Fuzzy Systems
Attenuation
Derivative
Output
Demonstrate

Keywords

  • Adaptive control
  • Fuzzy systems
  • Nonlinear systems

ASJC Scopus subject areas

  • Artificial Intelligence
  • Logic

Cite this

Mean-based fuzzy identifier and control of uncertain nonlinear systems. / Leu, Yih-Guang.

In: Fuzzy Sets and Systems, Vol. 161, No. 6, 16.03.2010, p. 837-858.

Research output: Contribution to journalArticle

@article{c29ab5742ce94cbaba0b7002860ab690,
title = "Mean-based fuzzy identifier and control of uncertain nonlinear systems",
abstract = "A mean-based adaptive fuzzy control scheme with state estimation performance is proposed for a class of uncertain nonlinear systems in the presence of only output measurement. In the control scheme, a mean-based fuzzy identifier without prior knowledge of membership functions is merged into direct adaptive controller with a linear state estimator. The structure of the mean-based fuzzy identifier is nonlinear in the adjusted parameters in order to diminish the unfavorable influence of initially designing membership functions on control performance. For the nonlinear structure, a mean method is used to derive adaptive laws. Compared with conventional methods, the advantage of the mean method is that the computation burden can be effectively alleviated because finding the derivative of fuzzy systems is not required. In addition, for the linear state estimator, the state estimation performance with beforehand given attenuation parameter is established by the design of a compensative controller. Finally, two examples are provided to demonstrate the applicability of the proposed scheme.",
keywords = "Adaptive control, Fuzzy systems, Nonlinear systems",
author = "Yih-Guang Leu",
year = "2010",
month = "3",
day = "16",
doi = "10.1016/j.fss.2009.09.013",
language = "English",
volume = "161",
pages = "837--858",
journal = "Fuzzy Sets and Systems",
issn = "0165-0114",
publisher = "Elsevier",
number = "6",

}

TY - JOUR

T1 - Mean-based fuzzy identifier and control of uncertain nonlinear systems

AU - Leu, Yih-Guang

PY - 2010/3/16

Y1 - 2010/3/16

N2 - A mean-based adaptive fuzzy control scheme with state estimation performance is proposed for a class of uncertain nonlinear systems in the presence of only output measurement. In the control scheme, a mean-based fuzzy identifier without prior knowledge of membership functions is merged into direct adaptive controller with a linear state estimator. The structure of the mean-based fuzzy identifier is nonlinear in the adjusted parameters in order to diminish the unfavorable influence of initially designing membership functions on control performance. For the nonlinear structure, a mean method is used to derive adaptive laws. Compared with conventional methods, the advantage of the mean method is that the computation burden can be effectively alleviated because finding the derivative of fuzzy systems is not required. In addition, for the linear state estimator, the state estimation performance with beforehand given attenuation parameter is established by the design of a compensative controller. Finally, two examples are provided to demonstrate the applicability of the proposed scheme.

AB - A mean-based adaptive fuzzy control scheme with state estimation performance is proposed for a class of uncertain nonlinear systems in the presence of only output measurement. In the control scheme, a mean-based fuzzy identifier without prior knowledge of membership functions is merged into direct adaptive controller with a linear state estimator. The structure of the mean-based fuzzy identifier is nonlinear in the adjusted parameters in order to diminish the unfavorable influence of initially designing membership functions on control performance. For the nonlinear structure, a mean method is used to derive adaptive laws. Compared with conventional methods, the advantage of the mean method is that the computation burden can be effectively alleviated because finding the derivative of fuzzy systems is not required. In addition, for the linear state estimator, the state estimation performance with beforehand given attenuation parameter is established by the design of a compensative controller. Finally, two examples are provided to demonstrate the applicability of the proposed scheme.

KW - Adaptive control

KW - Fuzzy systems

KW - Nonlinear systems

UR - http://www.scopus.com/inward/record.url?scp=74149087453&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=74149087453&partnerID=8YFLogxK

U2 - 10.1016/j.fss.2009.09.013

DO - 10.1016/j.fss.2009.09.013

M3 - Article

AN - SCOPUS:74149087453

VL - 161

SP - 837

EP - 858

JO - Fuzzy Sets and Systems

JF - Fuzzy Sets and Systems

SN - 0165-0114

IS - 6

ER -