• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.574-578
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• 2001

The stability analysis for the fuzzy logic controller (FLC) has widely been reported. Furthermore many research in the FLC has been introduced to decrease the number of parameters representing the antecedent part of the fuzzy control rule. In this paper we briefly explain a single-input fuzzy logic controller (SFLC) or simple-structured FLC which uses only a single input variable. And then we analyze that it is absolutely stale based on the sector bounded condition. We also show the feasibility of the proposed stability analysis through a numerical example of a mass-damper-spring system.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.1
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.342-345
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• 1995

This paper describes the design process and the experimental results of a fuzzy logic controller to control the tip position of a fixible-link manipulator, directly driven by a AC motor, with a large payload. The joint angle fuzzy logic controller is designed without a costly nonlinear system analysis of the flexible manipulator and the AC motor drive system. The state variables for the fuzzy logic controller are joint angle, joint velocity, link deflection, and link deflection velocity. The simulation and experimental results show that the joint position control is not satisfactory when the controller is designed under the assumption of no link flexibility and that stable joint position control and link vibration suppression can be cahieved with the fuzzy logic controller suggested in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.102.4-102
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• 2002

A deadzone compensator is designed for a positioning system using fuzzy logic. The classification property of fuzzy logic systems make them a natural candidate for the rejection of errors induced by the deadzone, which has regions in which it behaves differently. A tuning algorithm is given for the fuzzy logic parameters, so that the deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates, formal nonlinear stability proofs are given to show that the tracking error is small. The fuzzy logic deadzone compensator is implemented on a positioning system to show its efficacy. 1. Deadzone Compansation 2. XY positioning table 3. Fuzzy Logic 4. Actuator nonlinearity

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.361-365
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• 2004

In this paper, we present the modeling of time series data which are corrupted by noise via nonsingleton fuzzy logic system. Nonsingleton fuzzy logic system (NFLS) is useful in cases where the available data are corrupted by noise. NFLS is a fuzzy system whose inputs are modeled as fuzzy number. The abilities of NFLS to approximate arbitrary functions, and to effectively deal with noise and uncertainty, are used to analyze corrupted time series data. In the simulation results, we compare the results of the NFLS approach with the results of using only a traditional fuzzy logic system.

• Journal of the Korean Society of Safety
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• v.19 no.1
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• pp.49-55
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• 2004

This study presents an analysis of comparison of P-type fire detection system with fuzzy logic-applied fire detection system. The fuzzy logic-applied fire detection system has input variables obtained by fire experiment of small scale with K-type temperature sensor and optical smoke sensor. And the antecedent part of fuzzy rules consists of temperature and smoke density, and the consequent part consists of fire probability. Also triangular fuzzy membership function is used for input variables and fuzzy rules. To calculate the final fire probability a centroid method is introduced. A fire experiment is conducted with controlling wood crib layer, cigarette to simulate actual fire and false alarm situation. The results show that peak fire probability is 25[%] for non-fire and is more than 80[%] for fire situation, respectively. The fuzzy logic-applied fire detection system suggested here is able to distinguish fire situation and non-fire situation very precisely.

• Korean Journal of Logic
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• v.10 no.2
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• pp.125-146
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• 2007

This paper investigates the relevance system R-mingle (RM) as a a fuzzy-relevance logic. It shows that RM is fuzzy in Cintula's sense, i.e., RM is complete with respect to linearly ordered L-matrices (or L-algebras). More exactly, we first introduce RM and its weak versions wwRM and wRM. We next provide algebraic and matrix completeness results for them.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1510-1515
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• 2005

This paper is concerned with a Logic-based Fuzzy Neural Networks (LFNN) with the aid of fuzzy granulation. As the underlying design tool guiding the development of the proposed LFNN, we concentrate on the context-based fuzzy clustering which builds information granules in the form of linguistic contexts as well as OR fuzzy neuron which is logic-driven processing unit realizing the composition operations of T-norm and S-norm. The design process comprises several main phases such as (a) defining context fuzzy sets in the output space, (b) completing context-based fuzzy clustering in each context, (c) aggregating OR fuzzy neuron into linguistic models, and (c) optimizing connections linking information granules and fuzzy neurons in the input and output spaces. The experimental examples are tested through two-dimensional nonlinear function. The obtained results reveal that the proposed model yields better performance in comparison with conventional linguistic model and other approaches.

• Smart Structures and Systems
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• v.1 no.1
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• pp.13-27
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• 2005

Simultaneous precision positioning and vibration suppression of a reciprocating flexible manipulator is investigated in this paper. The flexible manipulator is driven by a multifunctional active strut with fuzzy logic controllers. The multifunctional active strut is a combination of a motor assembly and a piezoelectric stack actuator to simultaneously provide precision positioning and wide frequency bandwidth vibration suppression capabilities. First, the multifunctional active strut and the flexible manipulator are introduced, and their dynamic models are derived. A control strategy is then proposed, which includes a position controller and a vibration controller to achieve simultaneous precision positioning and vibration suppression of the flexible manipulator. Next, fuzzy logic control approach is presented to design a fuzzy logic position controller and a fuzzy logic vibration controller. Finally, experiments are conducted for the fuzzy logic controllers and the experimental results are compared with those from a PID control scheme consisting of a PID position controller and a PID vibration control. The comparison indicates that the fuzzy logic controller can easily handle the non-linearity in the strut and provide higher position accuracy and better vibration reduction with less control power consumption.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.67-72
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• 2013

Cloud detection and analysis from satellite images has been a topic of research in many atmospheric and environmental studies; however, it still is a challenging task for many reasons. In this paper, we propose a new method for cloud-type classification using fuzzy logic. Knowing that visible-light images of clouds contain thickness related information, while infrared images haves height-related information, we propose a two-layered fuzzy logic based on the input source to provide us with a relatively clear-cut threshold in classification. Traditional noise-removal methods that use reflection/release characteristics of infrared images often produce false positive cloud areas, such as fog thereby it negatively affecting the classification accuracy. In this study, we used the color information from source images to extract the region of interest while avoiding false positives. The structure of fuzzy inference was also changed, because we utilized three types of source images: visible-light, infrared, and near-infrared images. When a cloud appears in both the visible-light image and the infrared image, the fuzzy membership function has a different form. Therefore we designed two sets of fuzzy inference rules and related classification rules. In our experiment, the proposed method was verified to be efficient and more accurate than the previous fuzzy logic attempt that used infrared image features.