scholarly journals Development of a Didactic Educational Tool for Learning Fuzzy Control Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Juan Aguilera-Alvarez ◽  
José Padilla-Medina ◽  
Coral Martínez-Nolasco ◽  
Víctor Samano-Ortega ◽  
Micael Bravo-Sanchez ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Academic Performance ◽  
Fuzzy Control ◽  
Control Systems ◽  
Fuzzy Controller ◽  
Intelligent System ◽  
Knowledge Assessment ◽  
Teaching Tool ◽  
Virtual Instrumentation ◽  
Data Acquisition Board

This paper presents the development of a virtual didactic tool for students of mechatronic engineering taking an intelligent system course. The objective of the tool is for students to learn the structures for fuzzy control systems. This tool makes it easier for students to understand the behavior of the membership functions of input and output variables, the evaluation of the set of fuzzy rules, and the method of defuzzification, giving the students the possibility of applying a fuzzy controller in industrial processes using a data acquisition board. The proposed tool was developed with the virtual instrumentation software LabVIEW. It has the advantage that students can manipulate the internal structure of the fuzzy logic control system in a unique window where students can analyze the behavior of internal signals by looking at the response graphs. The fuzzy controller can be easily translated to a real application by using LabVIEW compatible hardware. To have feedback from students on the use of the tool and to understand if this tool allows an improvement in their academic performance, a 2-hour workshop on the proposed application was given to a group of 93 students. At the end of the workshop, a knowledge assessment and a perception survey were applied to the participants. The academic performance achieved by students who were given the workshop using the proposed teaching tool was compared with the academic performance of students who witnessed the workshop using Matlab tools. The statistical analysis of the results obtained for the knowledge assessment shows that the students that had taken the workshop using the proposed teaching tool had better compression of the topic compared to the students that had taken the workshop using the Fuzzy Logic Toolbox provided by Matlab MathWorks. The students that had taken the workshop using the proposed teaching tool obtained a mean grade of 89.63/100, while students that had taken the workshop using Matlab’s tools obtained a mean grade of 69.85/100. Also, the students’ perception of the proposed tool was that it allowed the design of fuzzy control systems in a simple and intuitive way.

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

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.

Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems

2001 ◽  
Vol 54 (6) ◽  
pp. B102-B103 ◽  
Author(s):  
Guanrong Chen, ◽  
Trung Tat Pham, ◽  
NM Boustany,
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Control ◽  
Fuzzy Sets ◽  
Control Systems

Control of Automobile's Automatic Parking

2011 ◽  
Vol 339 ◽  
pp. 28-31
Author(s):  
Hong Mei
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Control ◽  
Fuzzy Controller ◽  
Steering Wheel ◽  
Experienced Driver ◽  
Parking System ◽  
Automatic Parking ◽  
Conventional Methods ◽  
The Ideal

An automatic parking controller is proposed. Fuzzy control is taken to simulate the action of experienced driver as an alternative to conventional methods. The angle between the midline of the car and ideal path and the distance between the midpoint of the car and the ideal path are taken as the inputs of the fuzzy controller. The angle of the steering wheel is taken as the output of the fuzzy controller. A set of fuzzy logic rules are build for reasoning. With sensors installed in the car to replace people’s eyes and computer to replace people’s brain, the automatic parking system is more precise and quicker than human’s parking. At last, simulation is made and proved the validity of the proposed method.

scholarly journals Інтелектуальний регулятор запасу газодинамічної стійкості компресора авіаційного ГТД

2021 ◽  
pp. 48-52
Author(s):  
Сергій Васильович Єнчев ◽  
Сергій Олегович Таку
Keyword(s):  
Fuzzy Logic ◽  
Nonlinear Systems ◽  
Fuzzy Control ◽  
Control Systems ◽  
Gas Turbine ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Turbine Engine ◽  
Harmonic Linearization ◽  
Gas Dynamic

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.

scholarly journals A Philosophical Treatise and Educational Aspects on Fuzzy Control

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.

Professor Ebrahim Mamdani Memorial Issue

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 Zadehfs fuzzy logic and the development of fuzzy research. Prof. Mamdanifs two papers on fuzzy inference ? gApplication of fuzzy algorithms for control of simple dynamic planth (Proc. IEE, Vol.121, No. 12, pp. 1585-1588, 1974) and gAn experiment in linguistic synthesis with a fuzzy logic controllerh (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. Mamdanifs 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. Mamdanifs 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.

Research on Fuzzy Control System for Air-Condition in Bus

2012 ◽  
Vol 457-458 ◽  
pp. 998-1001
Author(s):  
Qiu Hua Miao ◽  
Pei Gang Jiao ◽  
Jie Liang
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Logic Control ◽  
Fuzzy Controller ◽  
Logic Control ◽  
Temperature Signal ◽  
Working Principle ◽  
Fuzzy Logic Technique ◽  
Deflection Rate

In this paper fuzzy logic control is introduced in auto adjust control system of air in bus. Fuzzy controller employs fuzzy control model of two inputs and one output. The paper also introduces the hardware framework, working principle and software of the system, analyzing merits of fuzzy logic technique, putting emphasis on the design of the fuzzy controller. In the controlling process, fuzzy controller samples temperature signal in bus and calculate deflection and deflection rate of temperature, looks up corresponding output in controlling table, and calculates its precision, then fuzzy controller gives related signal to motors and valve, acquiring satisfactory effect.

Control of a Fluidized Bed Combustor Using Fuzzy Logic

1996 ◽  
Vol 118 (1) ◽  
pp. 204-209 ◽  
Author(s):  
S. J. Koffman ◽  
R. C. Brown ◽  
R. R. Fullmer
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Control ◽  
Fluidized Bed ◽  
Fuzzy Logic Control ◽  
Fuzzy Controller ◽  
Experimental Results ◽  
Pi Control ◽  
Logic Control ◽  
And Performance ◽  
Fluidized Bed Combustor

Application of fuzzy logic control to a fluidized bed combustor (FBC) is examined. Major aspects of fuzzy control are reviewed, and design of a fuzzy controller for the FBC is described. Selected experimental results are presented, and performance of the fuzzy controller is evaluated through comparisons to results from classical PI control of the combustor.

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