Direct stable adaptive fuzzy neural model reference control of a class of nonlinear systems

Mojtaba Ahmadieh Khanesar; Mohammad Teshnehlab

doi:10.1109/ICICIC.2008.231

Direct stable adaptive fuzzy neural model reference control of a class of nonlinear systems

Mojtaba Ahmadieh Khanesar
, Mohammad Teshnehlab

K.N. Toosi University of Technology
IEEE

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

In this study, using a model reference adaptation law, a stable fuzzy neural control system is developed. Despite the advantages of Model reference control design technique, which is mainly its power to exactly set trajectories of the system under control, this method is designed for linear system. In this study using fuzzy neural systems, a stable model reference controller for nonlinear systems is developed. Lyapunov method is used to guarantee the stability of fuzzy neural training algorithm and model following of the system under control.

Original language	English
Title of host publication	3rd International Conference on Innovative Computing Information and Control, ICICIC'08
Publication date	2008
Article number	4603701
ISBN (Print)	9780769531618
DOIs	https://doi.org/10.1109/ICICIC.2008.231
Publication status	Published - 2008
Externally published	Yes
Event	3rd International Conference on Innovative Computing Information and Control - Dalian, Liaoning, China Duration: 18 Jun 2008 → 20 Jun 2008

Conference

Conference	3rd International Conference on Innovative Computing Information and Control
Country/Territory	China
City	Dalian, Liaoning
Period	18/06/2008 → 20/06/2008

Keywords

Fuzzy neural control
Model reference control
Stable controller

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10.1109/ICICIC.2008.231

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@inproceedings{5279d97c4a674dc2bf7c898b0b76486d,

title = "Direct stable adaptive fuzzy neural model reference control of a class of nonlinear systems",

abstract = "In this study, using a model reference adaptation law, a stable fuzzy neural control system is developed. Despite the advantages of Model reference control design technique, which is mainly its power to exactly set trajectories of the system under control, this method is designed for linear system. In this study using fuzzy neural systems, a stable model reference controller for nonlinear systems is developed. Lyapunov method is used to guarantee the stability of fuzzy neural training algorithm and model following of the system under control.",

keywords = "Fuzzy neural control, Model reference control, Stable controller",

author = "Khanesar, \{Mojtaba Ahmadieh\} and Mohammad Teshnehlab",

year = "2008",

doi = "10.1109/ICICIC.2008.231",

language = "English",

isbn = "9780769531618",

booktitle = "3rd International Conference on Innovative Computing Information and Control, ICICIC'08",

note = "3rd International Conference on Innovative Computing Information and Control, ICICIC 08 ; Conference date: 18-06-2008 Through 20-06-2008",

}

Khanesar, MA & Teshnehlab, M 2008, Direct stable adaptive fuzzy neural model reference control of a class of nonlinear systems. in 3rd International Conference on Innovative Computing Information and Control, ICICIC'08., 4603701, 3rd International Conference on Innovative Computing Information and Control, Dalian, Liaoning, China, 18/06/2008. https://doi.org/10.1109/ICICIC.2008.231

TY - GEN

T1 - Direct stable adaptive fuzzy neural model reference control of a class of nonlinear systems

AU - Khanesar, Mojtaba Ahmadieh

AU - Teshnehlab, Mohammad

PY - 2008

Y1 - 2008

N2 - In this study, using a model reference adaptation law, a stable fuzzy neural control system is developed. Despite the advantages of Model reference control design technique, which is mainly its power to exactly set trajectories of the system under control, this method is designed for linear system. In this study using fuzzy neural systems, a stable model reference controller for nonlinear systems is developed. Lyapunov method is used to guarantee the stability of fuzzy neural training algorithm and model following of the system under control.

AB - In this study, using a model reference adaptation law, a stable fuzzy neural control system is developed. Despite the advantages of Model reference control design technique, which is mainly its power to exactly set trajectories of the system under control, this method is designed for linear system. In this study using fuzzy neural systems, a stable model reference controller for nonlinear systems is developed. Lyapunov method is used to guarantee the stability of fuzzy neural training algorithm and model following of the system under control.

KW - Fuzzy neural control

KW - Model reference control

KW - Stable controller

U2 - 10.1109/ICICIC.2008.231

DO - 10.1109/ICICIC.2008.231

M3 - Article in proceedings

AN - SCOPUS:52449097983

SN - 9780769531618

BT - 3rd International Conference on Innovative Computing Information and Control, ICICIC'08

T2 - 3rd International Conference on Innovative Computing Information and Control

Y2 - 18 June 2008 through 20 June 2008

ER -

Direct stable adaptive fuzzy neural model reference control of a class of nonlinear systems

Abstract

Conference

Keywords

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