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Robust Fuzzy Logic Stabilization with Disturbance Elimination

A robust fuzzy logic controller is proposed for stabilization and disturbance rejection in nonlinear control systems of a particular type. The dynamic feedback controller is designed as a combination of a control law that compensates for nonlinear terms in a control system and a dynamic fuzzy logic...

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Autor principal: Danapalasingam, Kumeresan A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4142162/
https://www.ncbi.nlm.nih.gov/pubmed/25177713
http://dx.doi.org/10.1155/2014/171597
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author Danapalasingam, Kumeresan A.
author_facet Danapalasingam, Kumeresan A.
author_sort Danapalasingam, Kumeresan A.
collection PubMed
description A robust fuzzy logic controller is proposed for stabilization and disturbance rejection in nonlinear control systems of a particular type. The dynamic feedback controller is designed as a combination of a control law that compensates for nonlinear terms in a control system and a dynamic fuzzy logic controller that addresses unknown model uncertainties and an unmeasured disturbance. Since it is challenging to derive a highly accurate mathematical model, the proposed controller requires only nominal functions of a control system. In this paper, a mathematical derivation is carried out to prove that the controller is able to achieve asymptotic stability by processing state measurements. Robustness here refers to the ability of the controller to asymptotically steer the state vector towards the origin in the presence of model uncertainties and a disturbance input. Simulation results of the robust fuzzy logic controller application in a magnetic levitation system demonstrate the feasibility of the control design.
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spelling pubmed-41421622014-08-31 Robust Fuzzy Logic Stabilization with Disturbance Elimination Danapalasingam, Kumeresan A. ScientificWorldJournal Research Article A robust fuzzy logic controller is proposed for stabilization and disturbance rejection in nonlinear control systems of a particular type. The dynamic feedback controller is designed as a combination of a control law that compensates for nonlinear terms in a control system and a dynamic fuzzy logic controller that addresses unknown model uncertainties and an unmeasured disturbance. Since it is challenging to derive a highly accurate mathematical model, the proposed controller requires only nominal functions of a control system. In this paper, a mathematical derivation is carried out to prove that the controller is able to achieve asymptotic stability by processing state measurements. Robustness here refers to the ability of the controller to asymptotically steer the state vector towards the origin in the presence of model uncertainties and a disturbance input. Simulation results of the robust fuzzy logic controller application in a magnetic levitation system demonstrate the feasibility of the control design. Hindawi Publishing Corporation 2014 2014-08-06 /pmc/articles/PMC4142162/ /pubmed/25177713 http://dx.doi.org/10.1155/2014/171597 Text en Copyright © 2014 Kumeresan A. Danapalasingam. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Danapalasingam, Kumeresan A.
Robust Fuzzy Logic Stabilization with Disturbance Elimination
title Robust Fuzzy Logic Stabilization with Disturbance Elimination
title_full Robust Fuzzy Logic Stabilization with Disturbance Elimination
title_fullStr Robust Fuzzy Logic Stabilization with Disturbance Elimination
title_full_unstemmed Robust Fuzzy Logic Stabilization with Disturbance Elimination
title_short Robust Fuzzy Logic Stabilization with Disturbance Elimination
title_sort robust fuzzy logic stabilization with disturbance elimination
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4142162/
https://www.ncbi.nlm.nih.gov/pubmed/25177713
http://dx.doi.org/10.1155/2014/171597
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