Special Issue "Fuzzy Control Systems: Theory and Applications"

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Logic".

Deadline for manuscript submissions: closed (31 August 2021).

Special Issue Editor

Prof. Dr. Oscar Castillo
E-Mail Website
Guest Editor
Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22414, Mexico
Interests: fuzzy logic; type-2 fuzzy logic; fuzzy control; hybrid intelligent systems
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In 1965, Prof. L. Zadeh introduced the concept of Fuzzy Sets (FSs) to represent uncertain system parameters. However, in many real-world systems, uncertainty appears due to multiple reasons. In such a scenario, uncertainty modeling capabilities of the Type 1 (T1) or traditional FSs are quite limited. Due to this, Zadeh himself introduced the concept of Type-2 FSs in 1975. However, for more than a decade, these types of FSs received very little attention from the scientific community. Interestingly, from 1990, researchers started investigating T2 FSs, or more specifically Interval Type-2 (IT2) FSs, and successfully applied the same for realistic uncertainty modeling in a number of applications.

The aim of this Special Issue is to present the state-of-the-art results in the area of intelligent control theory and applications. Intelligent control is a technique of applying some methods to obtain a model of the process and using this model to design a controller. In particular, fuzzy control has been an important area of active research. Significant developments have been seen, including theoretical success and practical design. One of the reasons for the rapid growth of fuzzy control is its ability to control plants with uncertainties during its operation.

The papers in this Special Issue will present the most advanced techniques and algorithms of fuzzy control. These include various robust techniques, performance enhancement techniques, techniques with less a priori knowledge, and nonlinear intelligent adaptive control techniques. This Special Issue aims to provide an opportunity for international researchers to share and review recent advances in the foundations, integration architectures, and applications of hybrid and fuzzy systems in control.

The main aim of this Special Issue is to organize a forum to provide innovative approaches to handle various fuzzy controllers, adaptive control strategies, time-delay nonlinear systems, cooperative control, and hybrid intelligent control. We want to offer an opportunity for researchers and practitioners to identify new promising research directions in this area.

Prof. Dr. Oscar Castillo
Guest Editor

Manuscript Submission Information

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Keywords

  • Type-1 fuzzy control
  • Type-2 fuzzy control
  • fuzzy neural networks in control
  • metaheuristics in fuzzy control
  • evolutionary fuzzy control
  • genetic fuzzy control
  • fuzzy PID control
  • robot control

Published Papers (2 papers)

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Research

Article
Differential Evolution with Shadowed and General Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Optimal Design of Fuzzy Controllers
Axioms 2021, 10(3), 194; https://0-doi-org.brum.beds.ac.uk/10.3390/axioms10030194 - 19 Aug 2021
Viewed by 390
Abstract
This work is mainly focused on improving the differential evolution algorithm with the utilization of shadowed and general type 2 fuzzy systems to dynamically adapt one of the parameters of the evolutionary method. Previously, we have worked with both kinds of fuzzy systems [...] Read more.
This work is mainly focused on improving the differential evolution algorithm with the utilization of shadowed and general type 2 fuzzy systems to dynamically adapt one of the parameters of the evolutionary method. Previously, we have worked with both kinds of fuzzy systems in different types of benchmark problems and it has been found that the use of fuzzy logic in combination with the differential evolution algorithm gives good results. In some of the studies, it is clearly shown that, when compared to other algorithms, our methodology turns out to be statistically better. In this case, the mutation parameter is dynamically moved during the evolution process by using shadowed and general type-2 fuzzy systems. The main contribution of this work is the ability to determine, through experimentation in a benchmark control problem, which of the two kinds of the used fuzzy systems has better results when combined with the differential evolution algorithm. This is because there are no similar works to our proposal in which shadowed and general type 2 fuzzy systems are used and compared. Moreover, to validate the performance of both fuzzy systems, a noise level is used in the controller, which simulates the disturbances that may exist in the real world and is thus able to validate statistically if there are significant differences between shadowed and general type 2 fuzzy systems. Full article
(This article belongs to the Special Issue Fuzzy Control Systems: Theory and Applications)
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Article
An Efficient Chicken Search Optimization Algorithm for the Optimal Design of Fuzzy Controllers
Axioms 2021, 10(1), 30; https://0-doi-org.brum.beds.ac.uk/10.3390/axioms10010030 - 09 Mar 2021
Cited by 3 | Viewed by 754
Abstract
A proposed architecture to design the optimal parameters of Membership Functions (MFs) of Type-1 Fuzzy Logic Systems (T1FLSs) using the Chicken Search Optimization (CSO) is applied to three Fuzzy Logic Controllers (FLCs) in this paper. Two types of MFs are considered in the [...] Read more.
A proposed architecture to design the optimal parameters of Membership Functions (MFs) of Type-1 Fuzzy Logic Systems (T1FLSs) using the Chicken Search Optimization (CSO) is applied to three Fuzzy Logic Controllers (FLCs) in this paper. Two types of MFs are considered in the study: triangular and trapezoidal ones. The performance and efficiency of the CSO algorithm are particularly good when perturbations are added during the execution in each control problem. Two benchmark control problems: Water Tank Controller and Inverted Pendulum Controller are considered for testing the proposed approach. Also, the optimal design of a fuzzy controller for trajectory tracking of an Autonomous Mobile Robot (AMR) is considered to test the CSO. The main goal is to highlight the efficiency of CSO algorithm in finding optimal fuzzy controllers of non-linear plants. Two types of perturbations are considered in each control problem. Results show that the CSO algorithm presents excellent results in the field of Fuzzy Logic Controllers. Two types of Fuzzy Inference Systems: Takagi-Sugeno and Mamdani FLSs, are implemented in this paper. The most important metrics usually applied in control are used in this paper, such as: Integral Time Absolute Error (ITAE), Integral Time Squared Error (ITSE), Integral Absolute Error (IAE), Integral Square Error (ISE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). Full article
(This article belongs to the Special Issue Fuzzy Control Systems: Theory and Applications)
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