Comparative analysis of noise robustness of type 2 fuzzy logic controllers

Emanuel Ontiveros-Robles; Patricia Melin; Oscar Castillo

Kybernetika (2018)

  • Volume: 54, Issue: 1, page 175-201
  • ISSN: 0023-5954

Abstract

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Nowadays Fuzzy logic in control applications is a well-recognized alternative, and this is thanks to its inherent advantages as its robustness. However, the Type-2 Fuzzy Logic approach, allows managing uncertainty in the model. Type-2 Fuzzy Logic has recently shown to provide significant improvement in image processing applications, however it is also important to analyze its impact in controller performance. This paper is presenting a comparison in the robustness of Interval Type-2 and Generalized Type-2 Fuzzy Logic Controllers, in order to generate criteria to decide which type of controller is better in specific applications. The plants considered in the experimentation are two benchmark control plants and we report the Integral Squared Error (ISE), Integral Absolute Error (IAE) and Integral Time-weighted Absolute Error (ITAE) performance metrics, and also another important metric reported is the execution time. Based on the experimental results, Fuzzy Logic Controller selection criteria are proposed according to the performance and execution time requirements.

How to cite

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Ontiveros-Robles, Emanuel, Melin, Patricia, and Castillo, Oscar. "Comparative analysis of noise robustness of type 2 fuzzy logic controllers." Kybernetika 54.1 (2018): 175-201. <http://eudml.org/doc/294549>.

@article{Ontiveros2018,
abstract = {Nowadays Fuzzy logic in control applications is a well-recognized alternative, and this is thanks to its inherent advantages as its robustness. However, the Type-2 Fuzzy Logic approach, allows managing uncertainty in the model. Type-2 Fuzzy Logic has recently shown to provide significant improvement in image processing applications, however it is also important to analyze its impact in controller performance. This paper is presenting a comparison in the robustness of Interval Type-2 and Generalized Type-2 Fuzzy Logic Controllers, in order to generate criteria to decide which type of controller is better in specific applications. The plants considered in the experimentation are two benchmark control plants and we report the Integral Squared Error (ISE), Integral Absolute Error (IAE) and Integral Time-weighted Absolute Error (ITAE) performance metrics, and also another important metric reported is the execution time. Based on the experimental results, Fuzzy Logic Controller selection criteria are proposed according to the performance and execution time requirements.},
author = {Ontiveros-Robles, Emanuel, Melin, Patricia, Castillo, Oscar},
journal = {Kybernetika},
keywords = {interval Type-2 fuzzy logic; type-reduction; Type-2 fuzzy control; Type-2 fuzzy edge detection},
language = {eng},
number = {1},
pages = {175-201},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Comparative analysis of noise robustness of type 2 fuzzy logic controllers},
url = {http://eudml.org/doc/294549},
volume = {54},
year = {2018},
}

TY - JOUR
AU - Ontiveros-Robles, Emanuel
AU - Melin, Patricia
AU - Castillo, Oscar
TI - Comparative analysis of noise robustness of type 2 fuzzy logic controllers
JO - Kybernetika
PY - 2018
PB - Institute of Information Theory and Automation AS CR
VL - 54
IS - 1
SP - 175
EP - 201
AB - Nowadays Fuzzy logic in control applications is a well-recognized alternative, and this is thanks to its inherent advantages as its robustness. However, the Type-2 Fuzzy Logic approach, allows managing uncertainty in the model. Type-2 Fuzzy Logic has recently shown to provide significant improvement in image processing applications, however it is also important to analyze its impact in controller performance. This paper is presenting a comparison in the robustness of Interval Type-2 and Generalized Type-2 Fuzzy Logic Controllers, in order to generate criteria to decide which type of controller is better in specific applications. The plants considered in the experimentation are two benchmark control plants and we report the Integral Squared Error (ISE), Integral Absolute Error (IAE) and Integral Time-weighted Absolute Error (ITAE) performance metrics, and also another important metric reported is the execution time. Based on the experimental results, Fuzzy Logic Controller selection criteria are proposed according to the performance and execution time requirements.
LA - eng
KW - interval Type-2 fuzzy logic; type-reduction; Type-2 fuzzy control; Type-2 fuzzy edge detection
UR - http://eudml.org/doc/294549
ER -

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