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.

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.

scholarly journals Control of Rotary Cranes Using Fuzzy Logic

2001 ◽  
Author(s):  
Amjed A. Al-mousa ◽  
Ali H. Nayfeh ◽  
Pushkin Kachroo
Keyword(s):  
Fuzzy Logic ◽  
Closed Loop ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Operating Conditions ◽  
Building Construction ◽  
Loop Control ◽  
Industrial Structures ◽  
Inference Engines

Abstract Rotary cranes (tower cranes) are common industrial structures that are used in building construction, factories, and harbors. These cranes are usually operated manually. With the size of these cranes becoming larger and the motion expected to be faster, the process of controlling them became difficult without using automatic control methods. In general, the movement of cranes has no prescribed path. Cranes have to be run under different operating conditions, which makes closed-loop control preferable. In this work a fuzzy logic controller is introduced with the idea of split-horizon; that is, fuzzy inference engines (FIE) are used for tracking the position and others are used for damping the load oscillations. The controller consists of two independent controllers: radial and rotational. Each of these controllers has two fuzzy inference engines (FTEs). Computer simulations are used to verify the performance of the controller. Three simulation cases are introduced: radial, compound, and damping. The results from the simulations show that the fuzzy controller is capable of keeping the load-oscillation angles small throughout the maneuvers while completing them in a relatively reasonable time.

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

A New Fuzzy Inference Method for Symbolic Stability Analysis of Multigranular Intelligent Control System

2000 ◽  
Vol 12 (6) ◽  
pp. 664-674
Author(s):  
Hidehiro Yamamoto ◽  
◽  
Takeshi Furuhashi
Keyword(s):  
Control System ◽  
Stability Analysis ◽  
Fuzzy Control ◽  
Control Systems ◽  
Fuzzy Inference ◽  
Continuous System ◽  
Asymptotically Stable ◽  
Membership Functions ◽  
Symbolic System ◽  
Inference Method

Fuzzy inference has a multigranular architecture consisting of symbols and continuous values, and has worked well to incorporate experts' know-how into fuzzy controls. Stability analysis of fuzzy control systems is one of the main topics of fuzzy control. A recently proposed stability analysis method on the symbolic level opened the door to the design of stable fuzzy controller using symbols. However the validity of the stability analysis in the symbolic system is not guaranteed in the continuous system. To guarantee this validity, a nonseparate condition has been introduced. If the fuzzy control system is asymptotically stable in the symbolic system and the system satisfies the nonseparate condition, the continuous system is also asymptotically stable. However this condition is too conservative. The new condition called a relaxed nonseparate condition has been proposed and the class of control systems with guaranteed discretization has been expanded. However the relaxed condition has been applicable only to controf systems having symmetric membership functions. This paper presents a new fuzzy inference method that makes the relaxed condition applicable to fuzzy control systems with asymmetric membership functions. Simulations are done to demonstrate the effectiveness of the new fuzzy inference method. The proof of the expansion of the relaxed nonseparate condition is also given.

scholarly journals Control of Rotary Cranes Using Fuzzy Logic

2003 ◽  
Vol 10 (2) ◽  
pp. 81-95 ◽  
Author(s):  
Amjed A. Al-mousa ◽  
Ali H. Nayfeh ◽  
Pushkin Kachroo
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Operating Conditions ◽  
Building Construction ◽  
Loop Control ◽  
Industrial Structures ◽  
Inference Engines ◽  
First Case

Rotary cranes (tower cranes) are common industrial structures that are used in building construction, factories, and harbors. These cranes are usually operated manually. With the size of these cranes becoming larger and the motion expected to be faster, the process of controlling them has become difficult without using automatic control methods. In general, the movement of cranes has no prescribed path. Cranes have to be run under different operating conditions, which makes closed-loop control attractive.In this work a fuzzy logic controller is introduced with the idea of “split-horizon”; that is, fuzzy inference engines (FIE) are used for tracking the position and others are used for damping the load oscillations. The controller consists of two independent sub-controllers: radial and rotational. Each of these controllers has two fuzzy inference engines (FIE). Computer simulations are used to verify the performance of the controller. Three simulation cases are presented. In the first case, the crane is operated in the gantry (radial) mode in which the trolley moves along the jib while the jib is fixed. In the second case (rotary mode), the trolley moves along the jib and the jib rotates. In the third case, the trolley and jib are fixed while the load is given an initial disturbance. The results from the simulations show that the fuzzy controller is capable of keeping the load-oscillation angles small throughout the maneuvers while completing the maneuvers in relatively reasonable times.

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.

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