Modeling and control of uncertain nonlinear systems

Raheleh  Jafari; Sina  Razvarz; Alexander Gegov; Paul Satyam

doi:10.1109/IS.2018.8710463

Modeling and control of uncertain nonlinear systems

Raheleh Jafari, Sina Razvarz, Alexander Gegov, Paul Satyam

School of Computing

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

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Abstract

A survey of the methodologies associated with the modeling and control of uncertain nonlinear systems has been given due importance in this paper. The basic criteria that highlights the work is relied on the various patterns of techniques incorporated for the solutions of fuzzy equations that corresponds to fuzzy controllability subject. The solutions which are generated by these equations are considered to be the controllers. Currently, numerical techniques have come out as superior techniques in order to solve these types of problems. The implementation of neural networks technique is contributed in the complex way of dealing the appropriate coefficients and solutions of the fuzzy systems.

Original language	English
Title of host publication	2018 International Conference on Intelligent Systems (IS)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	168-173
ISBN (Electronic)	978-1-5386-7097-2
ISBN (Print)	978-1-5386-7098-9
DOIs	https://doi.org/10.1109/IS.2018.8710463
Publication status	Published - 9 May 2019
Event	9th International Conference on Intelligent Systems - Madeira Island, Portugal Duration: 25 Sept 2018 → 27 Sept 2018

Publication series

Name	IEEE IS Proceedings Series
Publisher	IEEE
ISSN (Print)	1541-1672

Conference

Conference	9th International Conference on Intelligent Systems
Abbreviated title	IS 2018
Country/Territory	Portugal
City	Madeira Island
Period	25/09/18 → 27/09/18

Keywords

nonlinear systems
uncertain systems
fuzzy equations
modelling
control

Access to Document

10.1109/IS.2018.8710463

IEEE_IS_2018_PAPER
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Cite this

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abstract = "A survey of the methodologies associated with the modeling and control of uncertain nonlinear systems has been given due importance in this paper. The basic criteria that highlights the work is relied on the various patterns of techniques incorporated for the solutions of fuzzy equations that corresponds to fuzzy controllability subject. The solutions which are generated by these equations are considered to be the controllers. Currently, numerical techniques have come out as superior techniques in order to solve these types of problems. The implementation of neural networks technique is contributed in the complex way of dealing the appropriate coefficients and solutions of the fuzzy systems.",

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Jafari, R, Razvarz, S, Gegov, A & Satyam, P 2019, Modeling and control of uncertain nonlinear systems. in 2018 International Conference on Intelligent Systems (IS). IEEE IS Proceedings Series, Institute of Electrical and Electronics Engineers Inc., pp. 168-173, 9th International Conference on Intelligent Systems, Madeira Island, Portugal, 25/09/18. https://doi.org/10.1109/IS.2018.8710463

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AU - Razvarz, Sina

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AU - Satyam, Paul

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N2 - A survey of the methodologies associated with the modeling and control of uncertain nonlinear systems has been given due importance in this paper. The basic criteria that highlights the work is relied on the various patterns of techniques incorporated for the solutions of fuzzy equations that corresponds to fuzzy controllability subject. The solutions which are generated by these equations are considered to be the controllers. Currently, numerical techniques have come out as superior techniques in order to solve these types of problems. The implementation of neural networks technique is contributed in the complex way of dealing the appropriate coefficients and solutions of the fuzzy systems.

AB - A survey of the methodologies associated with the modeling and control of uncertain nonlinear systems has been given due importance in this paper. The basic criteria that highlights the work is relied on the various patterns of techniques incorporated for the solutions of fuzzy equations that corresponds to fuzzy controllability subject. The solutions which are generated by these equations are considered to be the controllers. Currently, numerical techniques have come out as superior techniques in order to solve these types of problems. The implementation of neural networks technique is contributed in the complex way of dealing the appropriate coefficients and solutions of the fuzzy systems.

KW - nonlinear systems

KW - uncertain systems

KW - fuzzy equations

KW - modelling

KW - control

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DO - 10.1109/IS.2018.8710463

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Y2 - 25 September 2018 through 27 September 2018

ER -

Modeling and control of uncertain nonlinear systems

Abstract

Publication series

Conference

Keywords

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