Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems

The gas-dynamic stability of compressors of aircraft gas turbine engines is one of the most important conditions that determine their reliability and level of flight safety. Unstable operation of the compressor in the engine system (surge) leads to loss of thrust accompanied by an increase in gas temperature in front of the turbine and increased vibration because of large amplitudes of pressure pulsations and mass flow through the engine path. To improve the parameters of ACS aviation gas turbine engines are increasingly using regulators built using fuzzy logic algorithms. The implementation of fuzzy control algorithms differs from classical algorithms, which are based on the concept of feedback and reproduce a given functional dependence or differential equation. These functions are related to the qualitative assessment of system behavior, analysis of the current changing situation, and the selection of the most appropriate for the situation supervision of the gas turbine engine. This concept is called advanced management. ACS gas turbine engines with fuzzy regulators are nonlinear systems in which stable self-oscillations are possible. Approximate methods are used to solve the problems of analysis of periodic oscillations in nonlinear systems. Among them, the most developed in theoretical and methodological aspects is the method of harmonic linearization. The scientific problem is solved in the work – methods of synthesis of intelligent control system with the fuzzy regulator as a separate subsystem based on the method of harmonic linearization and design on its basis of fuzzy ACS reserve of gas-dynamic stability of aviation gas turbine engine. Based on the analysis of the principles of construction of fuzzy control systems, it is shown that the use of fuzzy logic provides a new approach to the design of control systems for aviation gas turbine engines in contrast to traditional control methods. It is shown that the fuzzy controller, as the only essentially nonlinear element when using numerical integration methods, can be harmonically linearized. Harmonic linearization allows using the oscillation index to assess the quality in the separate channels of fuzzy ACS aviation gas turbine engines. A fuzzy expert system has been developed for optimal adjustment of the functions of belonging of typical fuzzy regulators according to quality criteria to transients.

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A Philosophical Treatise and Educational Aspects on Fuzzy Control

WSEAS TRANSACTIONS ON SYSTEMS ◽

10.37394/23202.2020.19.33 ◽

2020 ◽

Vol 19 ◽

Keyword(s):

Fuzzy Logic ◽

Control System ◽

Fuzzy Control ◽

Control Systems ◽

Human Activity ◽

Input Data ◽

Feedback Controller ◽

Central Heating ◽

Almost All ◽

Heating Boiler

Fuzzy Logic has found nowadays many applications to almost all sectors of human activity, withfuzzy control being one of the most important such applications. A control system regulates the behavior of adevice or another system with the help of a feedback controller. A fuzzy control system is a control system thatanalyses the input data in terms of variables which take continuous values in the interval [0, 1]. The presentarticle studies in detail the operation of fuzzy control systems and illustrates it by presenting an exampleof controlling a building’s central heating boiler.

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Some Recent Defuzzification Methods

Theoretical and Practical Advancements for Fuzzy System Integration - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-1848-8.ch002 ◽

2017 ◽

pp. 31-48 ◽

Cited By ~ 2

Author(s):

Harendra Kumar

Keyword(s):

Fuzzy Logic ◽

Control System ◽

Fuzzy Control ◽

Fuzzy Sets ◽

Fuzzy Set ◽

Fuzzy Number ◽

Fuzzy Inference ◽

Fuzzy Logic System ◽

Single Number ◽

Logic System

Defuzzification is a process that converts a fuzzy set or fuzzy number into a crisp value or number. Defuzzification is used in fuzzy modeling and in fuzzy control system to convert the fuzzy outputs from the systems to crisp values. This process is necessary because all fuzzy sets inferred by fuzzy inference in the fuzzy rules must be aggregated to produce one single number as the output of the fuzzy model.There are numerous techniques for defuzzifying a fuzzy set; some of the more popular techniques are included in fuzzy logic system. In the present chapter some recent defuzzification methods used in the literature are discussed with examples.

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Some Recent Defuzzification Methods

Fuzzy Systems ◽

10.4018/978-1-5225-1908-9.ch044 ◽

2017 ◽

pp. 1003-1019

Author(s):

Harendra Kumar

Keyword(s):

Fuzzy Logic ◽

Control System ◽

Fuzzy Control ◽

Fuzzy Sets ◽

Fuzzy Set ◽

Fuzzy Number ◽

Fuzzy Inference ◽

Fuzzy Logic System ◽

Single Number ◽

Logic System

Defuzzification is a process that converts a fuzzy set or fuzzy number into a crisp value or number. Defuzzification is used in fuzzy modeling and in fuzzy control system to convert the fuzzy outputs from the systems to crisp values. This process is necessary because all fuzzy sets inferred by fuzzy inference in the fuzzy rules must be aggregated to produce one single number as the output of the fuzzy model.There are numerous techniques for defuzzifying a fuzzy set; some of the more popular techniques are included in fuzzy logic system. In the present chapter some recent defuzzification methods used in the literature are discussed with examples.

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Professor Ebrahim Mamdani Memorial Issue

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2012.p0591 ◽

2012 ◽

Vol 16 (5) ◽

pp. 591-591

Author(s):

Yoshinori Arai ◽

Toshihiko Watanabe

Keyword(s):

Fuzzy Logic ◽

Fuzzy Control ◽

Control Systems ◽

Fuzzy Logic Controller ◽

Fuzzy Inference ◽

Special Issue ◽

Fuzzy Algorithms ◽

Dynamic Plant ◽

Memorial Issue ◽

The Many

On February 22, 2010, Prof. Ebrahim H. Mamdani who devised Mamdani fuzzy inference has passed away. His work in fuzzy inference, which rapidly paved the way to its practical use, helped disseminate Prof. Lotfi Zadehfs fuzzy logic and the development of fuzzy research. Prof. Mamdanifs two papers on fuzzy inference ? gApplication of fuzzy algorithms for control of simple dynamic planth (Proc. IEE, Vol.121, No. 12, pp. 1585-1588, 1974) and gAn experiment in linguistic synthesis with a fuzzy logic controllerh (Int. J. of Man-Machine Studies, Vol.7, No.1, pp. 1-13, 1975) with S. Assilian ? enabled fuzzy inference technology to develop dramatically both indicatively and indirectly to where it has been applied, including fuzzy control systems. This special issue honors Prof. Mamdani for his invaluable efforts in these and many other fields. We have asked for submissions by researchers influenced by Prof. Mamdanifs achievements, including his work in fuzzy inference, and have narrowed down to one review and seven full papers. The review by Hirosato Seki and Kai Meng Tay provides an incisive overview on the many aspects of fuzzy inference that Prof. Mamdani brought to light. Prof. Mamdanifs fuzzy inference has become a deterministic technology that can be chosen naturally and that will continue to be influential and survivable well into the future.

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On Some Properties of Fuzzy Soft almost Soft Continuous Mappings

Moroccan Journal of Pure and Applied Analysis ◽

10.1515/mjpaa-2017-0011 ◽

2017 ◽

Vol 3 (2) ◽

pp. 131-139

Author(s):

Sabir Hussain

Keyword(s):

Decision Making ◽

Fuzzy Logic ◽

Information Systems ◽

Fuzzy Control ◽

Control Systems ◽

Continuous Mappings ◽

Fuzzy Automata ◽

Closed Mappings

AbstractIn this paper, we initiate and explore the interesting characterizations and properties of fuzzy soft almost soft continuous mappings in fuzzy soft classes. We also study and discuss the notions of fuzzy soft almost soft open(closed) mappings. Moreover the characterizations of composition of two fuzzy soft almost soft mappings are also studied. We hope that the findings in this paper will be useful for the researchers working in the fields such as fuzzy control systems, fuzzy automata, fuzzy logic, information systems and decision making problems.

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Investigation of the Possibility of Building a Neural Fuzzy Logic Controller with Discrete Terms for Controlling and Automating Oil and Gas Engineering Facilities

Intellekt Sist Proizv ◽

10.22213/2410-9304-2021-3-105-110 ◽

2021 ◽

Vol 19 (3) ◽

pp. 105-110

Author(s):

A. M. Sagdatullin ◽

Keyword(s):

Neural Network ◽

Fuzzy Logic ◽

Control System ◽

Fuzzy Control ◽

Control Systems ◽

Fuzzy Logic Controller ◽

Oil And Gas ◽

Fuzzy Controller ◽

Neuro Fuzzy ◽

Classical Control

The issue of increasing the efficiency of functioning of classical control systems for technological processes and objects of oil and gas engineering is investigated. The relevance of this topic lies in the need to improve the quality of the control systems for the production and transportation of oil and gas. The purpose of the scientific work is to develop a neuro-fuzzy logic controller with discrete terms for the control and automation of pumping units and pumping stations. It is noted that fuzzy logic, neural network algorithms, together with control methods based on adaptation and synthesis of control objects, make it possible to learn the automation system and work under conditions of uncertainty. Methods for constructing classical control systems are studied, the advantages and disadvantages of fuzzy controllers, as the main control system, are analyzed. A method for constructing a control system based on a neuro-fuzzy controller with discrete terms in conditions of uncertainty and dynamic parameters of the process is proposed. The positive features of the proposed regulator include a combination of fuzzy reasoning about a technological object and mathematical predictive models, a fuzzy control system gains the possibility of subjective description based on neural network structures, as well as adaptation to the characteristics of the object. The graph of dependence for the term-set of the controlled parameter on the degree of membership is presented. A possible implementation of tracking the triggering of one of the rules of the neuro-fuzzy system in the format of functional block diagrams is presented. The process of forming an expert knowledge base in a neuro-fuzzy control system is considered. For analysis, a graph of the dependence of the output parameter values is shown. According to the results obtained, the deviation of the values for the model and the real process does not exceed 18%, which allows us to speak of a fairly stable operation of the neuro-fuzzy controller in automatic control systems.

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Comparison of conventional rule based flow control with control processes based on fuzzy logic in a combined sewer system

Water Science & Technology ◽

10.2166/wst.2002.0665 ◽

2002 ◽

Vol 46 (6-7) ◽

pp. 77-84 ◽

Cited By ~ 12

Author(s):

K. Klepiszewski ◽

T.G. Schmitt

Keyword(s):

Fuzzy Logic ◽

Fuzzy Control ◽

Flow Control ◽

Control Systems ◽

Combined Sewer Overflow ◽

Sewer System ◽

Rule Based ◽

Combined Sewer ◽

Combined Sewer System ◽

Sewer Overflow

While conventional rule based, real time flow control of sewer systems is in common use, control systems based on fuzzy logic have been used only rarely, but successfully. The intention of this study is to compare a conventional rule based control of a combined sewer system with a fuzzy logic control by using hydrodynamic simulation. The objective of both control strategies is to reduce the combined sewer overflow volume by an optimization of the utilized storage capacities of four combined sewer overflow tanks. The control systems affect the outflow of four combined sewer overflow tanks depending on the water levels inside the structures. Both systems use an identical rule base. The developed control systems are tested and optimized for a single storm event which affects heterogeneously hydraulic load conditions and local discharge. Finally the efficiencies of the two different control systems are compared for two more storm events. The results indicate that the conventional rule based control and the fuzzy control similarly reach the objective of the control strategy. In spite of the higher expense to design the fuzzy control system its use provides no advantages in this case.

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