• Journal of Industrial Technology
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• v.16
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• pp.231-237
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• 1996

In this paper, the nonlinear I/O characteristic of fuzzy logic controller is analyzed by using cell concept. Sources of the nonlinearity in a fuzzy logic controller include the fuzzification, the fuzzy reasoning and the defuzzification. A closed form expression for the defuzzified output is derived in case of a fuzzy logic controller with two inputs, triangular memberships, MacVicar-Whelan type linguistic rules, and direct fuzzy reasoning. As a result, it is shown that fuzzy logic controller is a nonlinear controller. Also its nonlinearity is analyzed with respect to the conventional PID control and the sliding mode control.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.103-114
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• 1998

본 논문은 화력발전소 적용을 위한 경보처리 기반 고장진단 전문가 시스템(APDX(Alarm Processing and Diagnosis Expert System)개발에 관하여 논의한다. 본 연구에서 제시된 경보처리 알고리즘은 근본적으로는 경보 인과관계 트리를 사용하고 있으나 최종 원인 경보선택에 있어서는 경보 발생시간과 경보 우선순위 Meta-Rul를 활용한다. 경보처리 모듈에서 처리된 원인경보를 근거로 하여 본 원인경보와 관련된 고장부위를 진단하게 된다. 진단모듈에서는 경보에 관련된 센서들과 고장들 사이의 관계를 정상적으로 모델링하고 센서들의 트랜드를 정성적 해석기로 분석하여 증가, 정상, 감소의 세가지 상태에 대한 신뢰도를 출력한다. 또한 각 경보로부터 고장이 예상되는 고장타입을 센서 천이도로 모델링하여 진단에 활용된다. 최종적으로 추론모듈에서 퍼지(Fuzzy) 추론 알고리즘을 이용하여 모델된 고장 타입과 계산된 고장과의 매칭과정을 통하여 진단을 수행하게 되며, 계산 창 (Window)를 변경하면서 고장을 재 확인하게 된다.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.265-271
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• 1997

A fuzzy logic controller (FLC) has been widely used for many applications in recent years. But the relationship between control performance and design parameters has not been handled explicity in the conventional theory of fuzzy logic control. In this paper, based on the similarity between an FLC and a variable structure control (VSC) theory, a performance evaluation of an FLC, which gives quantitative accounts on the relationship is presented. The validity of the analysis is verified through extensive computer simulations.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1024-1026
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• 1996

Sources of nonlinearity In a fuzzy logic controller Include the fuzzification, the fuzzy reasoning and the defuzzification. In this paper, a closed form expression for the defuzzified output is derived in case of a fuzzy logic controller with two Inputs, triangular memberships, MacVicar-Whelan type linguistic rules, and direct fuzzy reasoning. As a result, it is shown that fuzzy logic controller is a nonlinear controller. Also its nonlinearity Is analyzed with respect to the conventional PID control and the sliding mode control.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.3033-3044
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• 1997

This paper presents a system which recognizes dynamic hand gesture for virtual reality (VR). A dynamic hand gesture is a method of communication for human and computer who uses gestures, especially both hands and fingers. Since the human hands and fingers are not the same in physical dimension, the produced by two persons with their hands may not have the same numerical values where obtained through electronic sensors. To recognize meaningful gesture from continuous gestures which have no token of beginning and end, this system segments current motion states using the state automata. In this paper, we apply a fuzzy min-max neural network and feature analysis method using fuzzy logic for on-line pattern recognition.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.257-263
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• 2005

There are some problems in TMA. There are no databases in Korea which can be independently and specially analyzed the TMA results. Even there are some medical databases, some of them are low level databases which are related to TMA, so they can not serve medical services to patients as well as doctors. Moreover, TMA results are based on the database of american health and mineral standards, it is possibly mislead oriental, especially korean, mineral standards. The purposes of this paper is to develope the first Intelligent TMA Information System(ITS) which makes clear the problems mentioned earlier ITS can analyze TMA data with multiple stage decision tree classifier. It is also constructed with multiple fuzzy rule base and hence analyze the complex data from Korean database by fuzzy inference methods.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.36 no.1
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• pp.54-59
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• 2000

This paper presents a control system that uses a fuzzy algorithm in controlling the depth of a model midwater trawl net, and experimental results carried out in the circulating water channel by using a model trawl winch system.The fuzzy controller calculates the length of the warp to be changed, based on the depth error between the desired depth and actual depth of the model trawl net and the ratio of change in the depth error. The error and the error change are calculated every sampling time. Then the control input, i.e. desirable length of the warp, is determined by inference from the linguistic control rules which an experienced captain or navigator uses in controlling the depth of the trawl winch controller and the length of the warp is changed. Two kinds of fuzzy control rules were tested, one was obtained from the actual operations used by a skilled skipper or navigator, and the other was a modified from the former by considering the hydrodynamic characteristics of the model trawl system.Two kinds of fuzzy control were tested, one was obtained fro the actual operations used by a skilled skipper or navigator, and the other was a modified from the former by considering the hydrodynamic characteristics of the model trawl system.The results of these model experiments indicate that the proposed fuzzy controllers rapidly follow the desired depth without steady-state error although the desired depth was given in one step, and show robustness properties against changes in the parameters such as the change of the towing sped. Especially, a modified rule shows smaller depth fluctuations and faster setting times than those obtained by a field oriented rule.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.303-311
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• 2012

In this study, we introduce a dynamic process model as well as the design methodology of optimized fuzzy controller for its efficient application to vacuum production system to produce a semiconductor, solar module and display and so on. In a vacuum control field, PID control method is widely used from the viewpoint of simple structure and preferred performance. But, PID control method is very sensitive to the change of environment of control system as well as the change of control parameters. Therefore, it's difficult to get a preferred performance results from target system which has a complicated structure and lots of nonlinear factors. To solve such problem, we propose the design methodology of an optimized fuzzy PID controller through a following series of steps. First a dynamic characteristic of the target system is analyzed through a series of experiments. Second the process model is built up and its characteristic is compared with real process. Third, the optimized fuzzy PID controller is designed using genetic algorithms. Finally, the fuzzy controller is applied to target system and then its performance is compared with that of other conventional controllers(PID, PI, and Fuzzy PI controller). The performance of the proposed fuzzy controller is evaluated in terms of auto-tuned control parameters and output responses considered by ITAE index, overshoot, rise time and steady state time.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.650-652
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• 2000

Although Fuzzy Logic Controller(FLC) adopted three terms as input gives better performance. FLC is in general composed of two-term control because of the difficulty in the construction of fuzzy rule base. In this paper, a three-term FLC which is similar to PID control but acts as a nonlinear controller is proposed. To reduce the complexity of the rule base design and increase efficiency, a simplified fuzzy PID control is induced from a hybrid velocity/position type PID algorithm by sharing a common rule base for both fuzzy Pi and fuzzy PD parts. It is simple in structure, easy in implementation, and fast in calculation. The phase plane technique is applied to obtain the rule base for fuzzy two-term control and them. The resultant rule base is Macvicar-Whelan type. The frequency response information is used in tuning of membership functions. Also a tuning strategy for the scaling factors is Proposed based on the relationship between PID gain and them. Simulation results show better performance and the effectiveness of the proposed method.

• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.1
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• pp.74-81
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• 2001

The sensorless scheme for Switched Reluctance Motor(SRM) drives must have the robustness and reliability because the noise and error are sensitive. These elements make electrically noisy environments due to the proximity of high current power circuits with small signal electronic circuits when SRM drives. Also, due to the leakage inductances and finite coupling capacitances, these can cause the noise on any low voltage current and voltage measurement circuit. The position estimate error occurs because the current and voltage including the noise are sued as the inputs of sensorless algorithm. In this paper the high robustness and resistance of input noise re described. The fuzzy logic based rotor estimation algorithm and the observer model are used to reduce the tolerance of input data.