Online Neuro-Fuzzy Controller: Design for Robust Stability
The Online Neuro-Fuzzy Controller (ONFC) is a fuzzy-based adaptive control that uses a very simple structure and can control nonlinear, time-varying and uncertain systems. Its efficiency and low computational cost allowed applications in several industrial plants successfully. However, none of the p...
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doaj-a6bbb965fa994ae7b72157af0a5f45402021-03-30T04:33:40ZengIEEEIEEE Access2169-35362020-01-01819376819377610.1109/ACCESS.2020.30334969239264Online Neuro-Fuzzy Controller: Design for Robust StabilityEverthon De Souza Oliveira0https://orcid.org/0000-0001-6016-9025Ricardo H. C. Takahashi1https://orcid.org/0000-0003-0814-6314Walmir Matos Caminhas2Department of Electrical Engineering, Federal Center for Technological Education of Minas Gerais, Belo Horizonte, BrazilDepartment of Mathematics, Federal University of Minas Gerais, Belo Horizonte, BrazilDepartment of Electronics Engineering, Federal University of Minas Gerais, Belo Horizonte, BrazilThe Online Neuro-Fuzzy Controller (ONFC) is a fuzzy-based adaptive control that uses a very simple structure and can control nonlinear, time-varying and uncertain systems. Its efficiency and low computational cost allowed applications in several industrial plants successfully. However, none of the previous works on the ONFC provided a design procedure endowed with formal guarantees of robust closed-loop stability. In this paper, some conditions for ONFC robust stability, considering system polytopic uncertainties, are presented using the Lyapunov method. A new adaptation rule is proposed that dynamically varies the adaptation gain and incorporates the dead-zone technique to ensure robustness to the noise measurement. A reference model is also introduced, in order to allow a direct specification of the closed-loop dynamics. Simulation results show that the new design conditions present good performance in the control of several types of systems.https://ieeexplore.ieee.org/document/9239264/Adaptive controlfuzzy controlONFCrobust stability |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Everthon De Souza Oliveira Ricardo H. C. Takahashi Walmir Matos Caminhas |
spellingShingle |
Everthon De Souza Oliveira Ricardo H. C. Takahashi Walmir Matos Caminhas Online Neuro-Fuzzy Controller: Design for Robust Stability IEEE Access Adaptive control fuzzy control ONFC robust stability |
author_facet |
Everthon De Souza Oliveira Ricardo H. C. Takahashi Walmir Matos Caminhas |
author_sort |
Everthon De Souza Oliveira |
title |
Online Neuro-Fuzzy Controller: Design for Robust Stability |
title_short |
Online Neuro-Fuzzy Controller: Design for Robust Stability |
title_full |
Online Neuro-Fuzzy Controller: Design for Robust Stability |
title_fullStr |
Online Neuro-Fuzzy Controller: Design for Robust Stability |
title_full_unstemmed |
Online Neuro-Fuzzy Controller: Design for Robust Stability |
title_sort |
online neuro-fuzzy controller: design for robust stability |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
The Online Neuro-Fuzzy Controller (ONFC) is a fuzzy-based adaptive control that uses a very simple structure and can control nonlinear, time-varying and uncertain systems. Its efficiency and low computational cost allowed applications in several industrial plants successfully. However, none of the previous works on the ONFC provided a design procedure endowed with formal guarantees of robust closed-loop stability. In this paper, some conditions for ONFC robust stability, considering system polytopic uncertainties, are presented using the Lyapunov method. A new adaptation rule is proposed that dynamically varies the adaptation gain and incorporates the dead-zone technique to ensure robustness to the noise measurement. A reference model is also introduced, in order to allow a direct specification of the closed-loop dynamics. Simulation results show that the new design conditions present good performance in the control of several types of systems. |
topic |
Adaptive control fuzzy control ONFC robust stability |
url |
https://ieeexplore.ieee.org/document/9239264/ |
work_keys_str_mv |
AT everthondesouzaoliveira onlineneurofuzzycontrollerdesignforrobuststability AT ricardohctakahashi onlineneurofuzzycontrollerdesignforrobuststability AT walmirmatoscaminhas onlineneurofuzzycontrollerdesignforrobuststability |
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1724181572764041216 |