• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.133.2-133
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• 2001

This paper focuses on the feasibility of fuzzy logic control for networked control systems. In order to evaluate its feasibility, a networked control system for motor speed control is implemented on a Profibus-DP network. The NCS consists of several independent, but interacting processes running on two separate stations. By using this NCS, the network delay is analyzed to find the cause of the delay. Furthermore, in order to prove the feasibility, the fuzzy logic controllers performance is compared with those of conventional PID controllers. Based on the experimental results, the fuzzy logic controller can be a viable choice for NCS due to its robustness against parameter uncertainty.

• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2008년도 춘계학술대회 학술발표회 논문집
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• pp.333-334
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• 2008

모든 이론에는 철학적인 전제가 있기 마련이다. 퍼지논리의 경우도 예외는 아니다. 본 논문은 역사적인 접근을 통해 퍼지논리가 상대주의와 다원주의적 세계관을 반영하고 있음을 보이는 것이 목적이다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.122-126
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• 1990

This paper discusses the possibility of applying fuzzy logic controller in a microprocessor-based brushless DC servo motor controller, which requires faster and more accurate response compared with other industrial processes. Limitations of fuzzy logic controller are also described.

• 한국지능시스템학회논문지
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• 제9권6호
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• pp.577-582
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• 1999

Using a sole input variable simplifies the design process for the fuzzy logic controller(FLC). This is called single-input fuzzy logic controller(SFLC). However it is still deficient in the capability of adapting to the varying operating conditions. We here design a single-input adaptive fuzzy logic controller(AFLC) using a switching function of the sliding mode control. The AFLC can directly incorporate linguistic fuzzy control rules into the controller. Hence some parameters of the membership functions characterizing the linguistic terms of the fuzzy rules can be adjusted by an adaptive law. In the proposed AFLC center values of fuzzy sets are directly adjusted by a fuzzy logic system. We prove that 1) its closed-loop system is globally stable in the sense that all signals involved are bounded and 2)its tracking error converges to zero asymptotically. We perform computer simulation using a nonlinear plant.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권4호
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• pp.217-231
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• 2015

A novel fuzzy learning framework that employs fuzzy inference to solve the problem of Multiple Instance Learning (MIL) is presented. The framework introduces a new class of fuzzy inference systems called Multiple Instance Mamdani Fuzzy Inference Systems (MI-Mamdani). In multiple instance problems, the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are known but not those of individual instances. MIL deals with learning a classifier at the bag level. Over the years, many solutions to this problem have been proposed. However, no MIL formulation employing fuzzy inference exists in the literature. Fuzzy logic is powerful at modeling knowledge uncertainty and measurements imprecision. It is one of the best frameworks to model vagueness. However, in addition to uncertainty and imprecision, there is a third vagueness concept that fuzzy logic does not address quiet well, yet. This vagueness concept is due to the ambiguity that arises when the data have multiple forms of expression, this is the case for multiple instance problems. In this paper, we introduce multiple instance fuzzy logic that enables fuzzy reasoning with bags of instances. Accordingly, a MI-Mamdani that extends the standard Mamdani inference system to compute with multiple instances is introduced. The proposed framework is tested and validated using a synthetic dataset suitable for MIL problems. Additionally, we apply the proposed multiple instance inference to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar.

• Journal of Electrical Engineering and information Science
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• 제2권6호
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• pp.14-19
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• 1997

In this paper, a new fuzzy sliding mode control algorithm is presented for trajectory control of robot manipulators. A fuzzy logic is applied to a sliding mode control algorithm to have the sliding mode gain adjusted continuously through fuzzy logic rules. With this scheme, te stability and the robustness of the proposed fuzzy logic control algorithm are proved and ensured by the sliding mode control law. The fuzzy logic controller requires only a few tuning parameters to adjust. Computer simulation results are given to show that the proposed algorithm can handle uncertain systems with large parameter uncertainties and external disturbances.

• 한국지능시스템학회논문지
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• 제7권1호
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• pp.34-44
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• 1997

The Belgian Nuclear Research Centre(SCK${\cdot}$CEN) has been a pioneer of the peaceful uses of nuclear energy after over forty years of existence. Recently, SCK${\cdot}$CEN's financial support of doctoral and postdoctoral research in close collaboration with universities has been a vital ingredient for securing a quality profile committed to the pursuit of execllence. FLINS, Fuzzy Logic and Intelligent technologies in Nuclear Science, was initially built within one of the postdoctoral research project at SCK${\cdot}$CEN. Among SCK${\cdot}$CEN's activities which will have an important impact on its scientific future, the application of fuzzy logic and intelligent technologies in nuclear science and engineering opens new domains in radiation protection, safety assessment, human reliability, nuclear reactor control, waste and disposal, etc. In this paper, we review the available literature on fuzzy logic in nuclear applications. We then present the initiative of R&D on fuzzy logic applications at SCK${\cdot}$CEN, namely, (1) safety control for a nuclear reactor, and (2) a safety evaluation model for nuclear transmission lines. By these two examples of nuclear applications, we illustrate the potential use of fuzzy logic in nuclear safety issues.

• 한국지능시스템학회논문지
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• 제4권2호
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• pp.35-40
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• 1994

Fuzzy logic controllers are often found to behave better than PI controllers. One of the major reasons for this is that the fuzzy logic inferences used can produce nonlinear type controllers. For some applicatioins, howeveer, linear fuzzy logic controllers also perofrm better than PI controllers. In this paper, we examine linear fuzzy logic controllers to show that the truncation effects of the fuzzy logic controllers make them perform much better than the PI controllers. In terms of a performance index we used, the truncation effects reduced the index value by up to 80% for examples we studied.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제3권1호
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• pp.93-99
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• 2003

One of the methods to simplify the design process for a fuzzy logic controller (FLC) is to reduce the number of variables representing the rule antecedent. This in turn decreases the number of control rules, membership functions, and scaling factors. For this purpose, we designed a single-input FLC that uses a sole fuzzy input variable. However, it is still deficient in the capability of adapting some varying operating conditions although it provides a simple method for the design of FLC's. We here design two simple-structured adaptive fuzzy logic controllers (SAFLC's) using the concept of the single-input FLC. Linguistic fuzzy control rules are directly incorporated into the controller by a fuzzy basis function. Thus some parameters of the membership functions characterizing the linguistic terms of the fuzzy control rules can be adjusted by an adaptive law. In our controllers, center values of fuzzy sets are directly adjusted by an adaptive law. Two SAFLC's are designed. One of them uses a Hurwitz error dynamics and the other a switching function of the sliding mode control (SMC). We also prove that 1） their closed-loop systems are globally stable in the sense that all signals involved are bounded and 2） their tracking errors converge to zero asymptotically. We perform computer simulations using a nonlinear plant.

• 제어로봇시스템학회논문지
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• 제8권4호
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• pp.281-287
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• 2002

This paper investigates on the feasibility of fuzzy logic control for networked control systems. In order to evaluate its feasibility, a networked control system for motor speed control is implemented on a Profibus-DP network. The NCS consists of several inde-pendent, but interacting processes running on two separate stations. By using this NCS, the network-induced delay is analyzed to find the cause and effect of the delay. Furthermore, in order to prove the feasibility, the fuzzy logic controller's performance is compared with those of conventional PID controllers. Based on the experimental results, the fuzzy logic controller can be a viable choice far NCS due to its robustness against parameter uncertainty.