A Conjugate Gradient Technique for Optimal Fuzzy Logic Control of Highly Complex Dynamic System

Khalifa, Iman; Elassal, A.H.; Saleh, Mujahed

doi:10.21608/iceeng.1998.60074

Cited by 2 publications

(3 citation statements)

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“…The commonly used defuzzification methods are the mean of maxima (MOM) and the center of area (COA) methods. Alternative defuzzification methods that are widely used in practical applications can be found in [8,11,12].…”

Section: Defuzzfication Techniquesmentioning

confidence: 99%

“…In the following part, the data for a 4 kW/220 V/1450 rpm, DC shunt motor are given [4,8] Ra=l Ω Rf=338Ω, β =0.0184 Nm/rad/sec K =2.2088 V/amp .rad/sec. La=5 mH Lf = 5 mH J = 0.05 Kg.m2 Hardware: 386-AT computer Software: The program is written using QB-language…”

Section: Numerical Examplementioning

confidence: 99%

“…The objective of such controller is to restore the motor speed through eliminating the speed error arising due to load changes. The only measurements required for the control design are only the output error and the change in the output error [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Improved Algorithm for a Dc Shunt Motor Using Different Deffuzzification Techniques

Al-Husban

2006

JES. Journal of Engineering Sciences

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This paper considers the fuzzy control of a non-linear dynamic system. The approach is known to be model-free and utilizes the field experience with running and supervising the process by human expertise. The advices given by the expert for proper running of these systems are given in the form of recipes full of linguistics. These linguistics can be turned into linguistic variables with limited sets of labels by the designer and to be the primitive seeds for what is called knowledge base. This base is kept in the memory of a computer unit. Assume that a running condition is given and has to be transferred into equivalent linguistic variables, in order to use the knowledge base and induce the output in a form of linguistic variables. Knowing that the linguistic variables is the same as fuzzy variables, the process of a fuzzy control can be stated as the three step procedure, a fuzzification stage, an inference stage, and finally a defuzzification stage, in which a crisp value is obtained for the process control input. The defuzzification stage is not unique and different techniques have been proposed in the literature. Starting from the centroid method up to the maximum principle method, the methods vary between themselves in complexity and time consumed by the computer unit. This, in turn, could result in different time responses for the given nonlinear dynamic systems. The paper concentrates on the effect of the defuzzification techniques on the transient response of a DC shunt motor taken as a model example of non-linear systems. A comparative study is given supported by computer simulation for each case.

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Section: Defuzzfication Techniquesmentioning

confidence: 99%

Section: Numerical Examplementioning

confidence: 99%