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Mathematics
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Fuzzy Logic Control Systems
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Fuzzy Logic Control Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 2500

Special Issue Editors

Prof. Dr. Mihaela Colhon

E-Mail Website
Guest Editor
Department of Computer Science, University of Craiova, 200585 Craiova, Romania
Interests: artificial intelligence; natural language processing; knowledge representation
Special Issues, Collections and Topics in MDPI journals
Dr. Camelia Delcea

E-Mail Website1 Website2
Guest Editor
Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 010552 Bucharest, Romania
Interests: cybernetics; fuzzy theory; grey systems theory; operations research; strategic management; computational intelligence; business analysis; agent-based modelling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maikel Yelandi Leyva Vázquez

E-Mail Website
Guest Editor
Universidad Politécnica Salesiana , Chambers 227 y 5 de Junio , Guayas, Guayaquil 090101, Ecuador
Interests: softcomputing; fuzzy cognitive maps; artificial intelligence; neutrosophics; machine learning

Special Issue Information

Dear Colleagues,

This Special Issue of the open-access journal Mathematics (ISSN 2227-7390) will publish new results in the field of fuzzy logic control systems using theories, concepts, and algorithms that extend the original ideas of the pioneering work of Prof. L. A. Zadeh. Traditional fuzzy logic or type-1 fuzzy logic has been widely used in various decision-making models using different fuzzy-logic based approaches.

Mainly because fuzzy logic has emerged as a powerful representation method in handling vague estimates, several logics were developed to capture fuzziness, imprecision, and uncertainty of data.

In the seventies, Zadeh, Grattan-Guinness, Jahn, and Sambuc independently introduced interval-valued fuzzy sets (IVFS) in which the set membership is treated as an interval.

Later, Belnap defined four-valued logic to cope with multiple information sources.

Rough set theory was developed by Pawlak in the eighties, which was found useful for decision-making applications in different domains.

K. Atanassov extended fuzzy logic to Intuitionistic fuzzy sets (IFS) and then, to allow greater freedom and flexibility in representing uncertainty, interval-valued intuitionistic fuzzy sets (IVIFS) were proposed.

Vague sets defined as sets of objects having a grade of membership value as a continuous subinterval of [0, 1] were introduced in the literature by Gau and Buehrer in the nineties.

In 1998 F. Smarandache proposed the Neutrosophic Logic and, quite recently (in 2013), refined neutrosophic logic (which generalizes Belnap's four-valued logic) to represent mathematical models of uncertainty, vagueness, ambiguity, imprecision, incompleteness, inconsistency, redundancy, and contradiction.

For this Special Issue, authors are invited to publish new theories, concepts, and algorithms related to all these fuzzy logic extensions in conjunction with control systems development and with linguistic knowledge deduced from expert knowledge on control issues.

Prof. Dr. Mihaela Colhon
Prof. Camelia Delcea
Prof. Dr. Maikel Yelandi Leyva Vázquez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Fuzzy mathematics for fuzzy controllers
  • Fuzzy modeling
  • Takagi–Sugeno structures
  • Optimization-based fuzzy control
  • Fuzzy algorithms for search, classification, approximation, and learning
  • Type 2 fuzzy controllers
  • Intuitionistic fuzzy logic control
  • Neutrosophic logic control
  • Fuzzy refined neutrosophic logic control
  • Picture fuzzy logic control
  • Intelligent control
  • Fuzzy expert systems
  • Technologies and applications
  • Implementation of fuzzy control structures in fuzzy control languages
  • Industrial application of fuzzy control

Published Papers (1 paper)

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Research

19 pages, 470 KiB  
Article
Relaxed Observer-Based H∞-Control for Markov Jump Fuzzy Systems with Incomplete Transition Probabilities and Sensor Failures
by Thanh Binh Nguyen and Hyoung-Kyu Song
Mathematics 2022, 10(12), 2055; https://0-doi-org.brum.beds.ac.uk/10.3390/math10122055 - 14 Jun 2022
Cited by 2 | Viewed by 1339
Abstract
This paper is concerned with linear matrix inequality conditions to design observer-based H∞-controllers for discrete-time Markov jump fuzzy systems with regard to incomplete transition probabilities and sensor failures. Since some system states involved in fuzzy premise variables are immeasurable or under [...] Read more.
This paper is concerned with linear matrix inequality conditions to design observer-based H∞-controllers for discrete-time Markov jump fuzzy systems with regard to incomplete transition probabilities and sensor failures. Since some system states involved in fuzzy premise variables are immeasurable or under sensor failures, the observer-based fuzzy controller does not share the same fuzzy basic functions with plants, leading to a mismatch phenomenon. Our work contributes a new single-step LMI method for synthesizing the observer-based controller of the Markov jump fuzzy system in the presence of sensor failures with regard to the mismatched phenomenon. The non-convex H∞-stabilization conditions induced by the output-feedback scheme are firstly formulated in terms of multiple-parameterized linear matrix inequalities (PLMIs). Secondly, by assuming that the differences of fuzzy basic functions between the controller and plant are bounded, the multi-PLMI-based conditions are cast into linear matrix inequalities standing for tractable conditions. The designed observer-based controller guarantees the stochastic stability of the closed-loop system and less conservative results compared to existing works in three numerical examples. Full article
(This article belongs to the Special Issue Fuzzy Logic Control Systems)
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