• Journal of Families and Better Life
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• v.6 no.1
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• pp.167-187
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• 1988

The purpose of this study is to explore whether wives communicate differently over the family life cycle(FLC), and whether their communication contents and frequencies are differently over the FLC. Marital communication scales are composed of six subfactors such as The selectivity of channel (SC), Channel direction (CD), communication style(CS), Comprehension of nonverbal communication (CNC), Communication contents and frequencies(CCF) and Olson's marital communication (LCQ). The number of the subjects are 582, who are living in Pusan in 1987 and they divided into six stages family life cycle; The newly married (FLC 1). The family with preschoolers(FLC 2), The family with schoolages (FLC3), The family with teenages(FLC4)， The family with launching children(FLC 5) and The postparenthood (FLC 6). This study is surveyed by Frequencies and Mean score from six subfactors and tested by chi-square test and ANOVA. the results are as follows; 1) There are a few significant differences in CS and CNC by FLC. The wives from FLC 1 and FLC 2 use the most open communication style and them form FLC5 use the most closed style. There are revealed that comprehension of nonverbal communication is the highest of FLC2, but on FLC 1 it is the lowest. 2) There are significant difference in CCF. Children, Wive's life, Husband's life, Affection and sex and Empathy show significant differences over the over the FLC. Regardless of FLC, there are conclueded that Children, Wive's and husband's life and economy management are the most frequence communicative contents. These results suggest that there needs to be further investigation on Olson's marital communication scale for comparing another country with ours and on the training and programs for couple's skillful communication ability.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1034-1036
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• 1996

In this paper, an adaptive fuzzy logic controller is presented for auto-tunning of the scaling factors by using learning capability of neural networks. The proposed scheme consists of the FLC which includes the PI-type FLC and PD-type FLC in parallel form and the neural network which learns scale factors of FLC. Computer simulations were performed to illustrate the effectiveness of a proposed scheme. A proposed FLC controller was applied to the second order system and velocity control of the brushless DC motors. For the design of the FLC, tracking error, change of error, and acceleration error are selected as input variables of the FLC and three seal e factors were used in the parallel-type FLC. This scheme can be used to reduce the difficulty in the selection of the scale factors.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.525-529
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• 2009

Most of FLC received input data from error e and change-of-error e' with no relation with system complexity. Basic scheme follows typical PD and PI or PID Controller and that has been developed through fixed ME In this paper, We studied the relationship between MF and system response and system response through changing Fuzzy variable of consequence MF and propose the simple FLC using this relationship. The response of FLC is changed according to the width of Fuzzy variable of consequence MF. As changing the Fuzzy variable of consequence MF shows various nonlinear characteristic, we studied the relation between response and MF using analytical method. We designed the effective FLC using three-variable MF and nine rules and took simulation for verification. In this study, we propose the method to design system with FLC in stability point which is an impotent characteristic of designing system. The circle criterion which is adapted to analysis the nonlinear system is put to use for proposed method. Since SISO FLC has a time-invariant and odd characteristic we can use the critical point not disk which is generally used to determine the stability in the circle criterion, to determine the stability. Using this, we can get the maximum critical point plot of SISO FLC with changing the consequence fuzzy variables. The predetermined critical point plot of FLC can be used to decide the region of the system to be stable. This method is effectively used to design the SISO FLC.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.5
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• pp.14-20
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• 1997

This paper proposes a new design method for the SMC-type FLC and shows that a SMC-type LFC is an extension of the SMC with BL. The conventional SMC-type FLC uses error and change-of-error as inputs of the FLC and generates the absolute value of a switching magnitude. Then, the fuzzy rule table is constructed on a two-dimensional space of the phase plane and has commonly the skew symmetric property. In this paper, we introduce a new variable, signed distance, from the skew symmetric property of the rule table. And thd variable becomes only a fuzzy variable that is used to generate the control input of a SMC-type FLC. that is, we design a new SMC-type FLC that uses a signed distance and a control input as the variables representing the contents of the rule-antecedent and the rule-con-sequent, respectively. Then the number of total rules is reduced and the control performance is almost the same as that of the conventional SMC-type FLC. Additionally, we derive the control law of the ordinary SMC with BL from a new SMC-type FLC. Namely, we show that a FLC is an extension of the SMC with BL.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1080-1085
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• 1993

A nonlinear control algorithm for the depth control of underwater vehicles is presented. In order to consider the deadzone effect of the flow control valve, a nonlinear fuzzy logic controller (FLC) is synthesized and combined with a linear proportional-derivative-acceleration (PDA) controller, which is called, the PDA/FLC controller. And, to show the effectiveness of the PDA/FLC control system, it is compared with the linear PDA control system through computer simulation. It is found that the PDA/FLC control scheme is a suitable one to maintain the desirable depth of underwater vehicles with deadzone.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.9
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• pp.897-901
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• 2011

This paper presents a TS (Takagi-Sugeno) type FLC (Fuzzy Logic Controller) with only 3 rules. The choice of parameters of FLC is very difficult job on design FLC controller. Therefore, the choice of appropriate linguistic variable is an important part of the design of fuzzy controller. However, since fuzzy controller is nonlinear, it is difficult to analyze mathematically the affection of the linguistic variable. So this choice is depend on the expert's experience and trial and error method. In the design of the system, we use a variety of response characteristics like stability, rising time, overshoot, settling time, steady-state error. In particular, it is important for a stable system design to predict the steady-state error because the system's steady-state response of the system is related to the overall quality. In this paper, we propose the method to choose the consequence linear equation's parameter of T-S type FLC in the view of steady-state error. The parameters of consequence linear equations of FLC are tuned according to the system error that is the input of FLC. The full equation of T-S type FLC is presented and using this equation, the relation between output and parameters can represented. As well as the FLC parameters of consequence linear equations affect the stability of the system, it also affects the steady-state error. In this study, The system according to the parameter of consequence linear equations of FLC predict the steady-state error and the method to remove the system's steady-state error is proposed using the prediction error value. The simulation is carried out to determine the usefulness of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1101-1105
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• 2012

This study proposes a method for improvement of PD type fuzzy controller. The method includes self tuner using gradient algorithm that is one of the optimization algorithms. The proposed controller improves simple Takagi-Sugeno type FLC (Fuzzy Logic Control) system. The simple Takagi-Sugeno type FLC system changes nonlinear characteristic to linear parameters of consequent membership function. The simple FLC system could control the system by calibrating parameter of consequent membership function that changes the system response. While the determination on parameter of the simple FLC system works well only partially, the proposed method is needed to determine parameters that work for overall response. The simple FLC system doesn't predict the response characteristics. While the simple FLC system works just like proportional part of PID, our system includes derivative part to predict the next response. The proposed controller is constructed with P part and D part FLC system that characteristic parameter on system response is changed by self tuner for effective response. Since the proposed controller doesn't include integral part, it can't eliminate steady state error. So we include a gain to eliminate the steady state error.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.264-268
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• 2011

This paper presents a Takagi-Sugeno (T-S) type Fuzzy Logic Controller (FLC) with only 3 rules. The choice of parameters of FLC is very difficult job on design FLC. Therefore, the choice of appropriate linguistic variable is an important part of the design of fuzzy controller. However, since fuzzy controller is nonlinear, it is difficult to analyze mathematically the affection of the linguistic variable. So this choice is depend on the expert's experience and trial and error method. In this paper, we propose the method to choose the consequence linear equation's parameter of T-S type FLC. The parameters of consequence linear equations of FLC are tuned according to the system error that is the input of FLC. The full equation of T-S type FLC is presented and using this equation, the relation between output and parameters can represented. The parameters are tuned with gradient algorithm. The parameters are changed depending on output. The simulation results demonstrate the usefulness of this T-S type 3 rule fuzzy controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.4
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• pp.58-64
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• 1997

FLC(Fuzzy Logic Controller) is stronger to the disturbance than a classical controller and its overshoot of the intialized value is excellent. In case an unknown process or the mathematical modeling of a complicated system is impossible, a fit control quantity can be acquired by the Fuzzy inference. But FLC can not converge correctly to the desirable value because the FLC's output value by the size of the quantization level of the Fuzzy variable always has a minor error. There are many ways to eliminate the minor error, but I will suggest GA-FLC and EP-FLC Hybrid controller which csombines FLC with GA(Genetic Algorithm) and EP(Evo1ution Programming). In this paper, the output characteristics of this Hybrid controller will be compared and analyzed with those of FLC, it will he showed that this Hybrid controller converge correctly to the desirable value without any error, and !he convergence speed performance of these two kinds of Hyhrid controller also will be compared.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.55 no.3
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• pp.117-122
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• 2006

The paper is proposed artificial neural network(ANN) sensorless control of induction motor drive with fuzzy learning control-fuzzy neural network(FLC-FNN) controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also this paper is proposed. speed control of induction motor using FLC-FNN and estimation of speed using ANN controller. The back Propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled FLC-FNN and ANN controller, Also, this paper is proposed the analysis results to verify the effectiveness of the FLC-FNN and ANN controller.