• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.44-49
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• 2001

For minimum phase systems, the conventional fuzzy logic controllers (FLCs) use the error and the change-of-error as fuzzy input variables. Then the control rule table is a skew symmetric type, that is, it has UNLP (Upper Negative and Lower Positive) or UPLN property. This property allowed to design a single-input FLC (SFLC) that has many advantages. But its control parameters are not automatically adjusted to the situation of the controlled plant. That is, the adaptability is still deficient. We here design a single-input direct adaptive FLC (SDAFLC). In the AFLC, some parameters of the membership functions characterizing the linguistic terms of the fuzzy rules are adjusted by an adaptive law. The SDAFLC is designed by a stable error dynamics. We prove that its closed-loop system is globally stable in the sense that all signals involved are bounded and its tracking error converges to zero asymptotically. We perform computer simulations using a nonlinear plant and compare the control performance between the SFLC and the SDAFLC.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.31-34
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• 2002

It studied modelling about employing analog PSS in domestic 500MW grade standard thermoelectric power plant in this paper. Modeling and parameters were verified by simulation of PSS response characteristics measured in AVR step examination and using power plant system model parameters decided by generator characteristics test. Through this PSS modelling research, contributed in domestic electrical power system small signal stability as that do modelling to be possible exact analysis of power system.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.519-521
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• 1999

An adaptive fuzzy controller is constructed from a set of fuzzy IF-THEN rules whose parameters are adjusted on-line according to some adaptation law for the purpose of controlling the plant to track a given trajectory. Adaptive fuzzy controller of this paper is designed based on the Lyapunov synthesis approach The adaptive fuzzy controller is designed through the following steps: first, construct an initial controller based on linguistic descriptions(in the form of fuzzy IF-THEN rules) about the unknown plant from human experts; then, develop an adaptation law to adjust the parameters of the fuzzy controller on-line, the adaptive fuzzy controllers are used to control the inverted pendulum to track a given trajectory.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.6
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• pp.45-51
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• 1995

This paper proposes a CMAC-based controller for servo systems with stick-slip friction. Performance of the controller was evaluated from computer simulations and compared with that of a conventional PID controller. Firction model used in the simulations is based upon the one proposed by Tustin. It was shown that the CMAC-based controller settles more quickly, and overshoots less than the PID. It was also shown that the CMAC is less sensitive to the changes of the plant parameters.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.10
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• pp.827-832
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• 2002

We propose a prediction method of the pollutant and a synchronous classification of the current state of SOx emission in the power plant. We use the auto-regressive with exogeneous (ARX) model as a predictor of SOx emission and use a radial basis function network (RBFN) as a pattem classifier. The ARX modeling scheme is implemented using recursive least squares (RLS) method to update the model parameters adaptively. The capability of SOx emission monitoring is utilized with the application of the RBFN classifier. Experimental results show that the ARX model can predict the SOx emission concentration well and ARX modeling parameters can be a good feature for the state monitoring. in addition, its validity has been verified through the power spectrum analysis. Consequently, the RBFN classifier in combination with ARX model is shown to be quite adequate for monitoring the state of SOx emission.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.459-464
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• 2001

For major nuclear power plant components periodic inspections and integrity assessments are needed for the safety. But many flaws are undetectable due to sampling inspection. Probabilistic integrity assessment is applied to take into consideration of uncertainty and variance of input parameters arise due to material properties, applied load and undetectable flaws. This paper describes a Probabilistic Fracture Mechanics(PFM) analysis based on Monte Carlo(MC) algorithms. Taking important parameters as probabilistic variables such as fracture toughness, crack growth rate and flaw shape, failure probability of major nuclear power plant components is archived as a results of MC simulation. For the verification of these analysis, a comparison study of the PFM analysis using other commercial code, mathematical method is carried out and a good agreement was observed between those results.

• Journal of Power System Engineering
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• v.9 no.4
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• pp.194-201
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• 2005

In this paper, we propose an adaptive mechanism based on humoral immune algorithm and neural network identifier technique. It is also applied for an autonomous guided vehicle (AGV) system. When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged due to the abrupt change of PID parameters since the parameters are almost adjusted randomly. To slove this problem, we use the neural network identifier technique for modeling the plant humoral immune algorithm (HIA) which performs the parameter tuning of the considered model, respectively. Finally, the experimental results for control of steering and speed of AGV system illustrate the validity of the proposed control scheme. Also, these results for the proposed method show that it has better performance than other conventional controller design method such as PID controller.

• Nuclear Engineering and Technology
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• v.41 no.10
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• pp.1255-1262
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• 2009

This paper introduces the status of and issues related to the PSHA (Probabilistic Seismic Hazard Analysis) of Korean Nuclear Power Plant sites. PSHA was first introduced to the nuclear industry in the mid-1980s. The Korean PSHA is based on Cornell and accommodates the modem approach for eliciting expertise and statistical treatment. Due to the low seismicity in Korea, large uncertainties exist in the PSHA database including seismic source maps, seismicity parameters of seismic sources, and attenuation formulae. Though research in seismology, geology, and earthquake engineering since the mid-1990s has significantly reduced uncertainties, a considerable amount still exists. Considering the low seismicity of the Korean Peninsula, especially the lack of strong motion data, further reduction will take several decades.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.36-42
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• 1996

In this paper, we proposed a PID controller, which could control unknown plants using Artificial Neural Network(ANN) for auto-tuning of the PID parameters. In the proposed algorithm, the parameters of the controller were adjusted to reduce the error of the controlled plant. In this process, the sensitivity between input and output of the unknown plant was needed. So, in order to obtain this sensitivity, the ANN's learnig ability was used. Computer simualtions were performed for the regulation problems, and the results were compared with those of Ziegler-Nichols PID controller. As a result, it was shown that the proposed algorithm outperformed Ziegler-Nichols controller in rise time, overshoot, undershoot, and setting time.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.7
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• pp.53-64
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• 2001

The gain-scheduling control technique is vary useful in the control problem incorporating time varying parameters which can be measured in real time. Based on these facts, in this paper the sway control problem of the pendulum motion of a container hanging on the trolly, which transports containers from a container ship to trucks, is considered. In the container crane control problem, suppressing the residual swing motion of the container at the end of acceleration, deceleration or the case of that the unexpected disturbance input exists is main issue. For this problem, in general, the trolley motion control strategy is introduced and applied. But, in this paper, we introduce and synthesize a new type of swing motion control system. In this control system, a small auxiliary mass is installed on the spreader. And the actuator reacts against the auxiliary mass, applying inertial control forces to the container to reduce the swing motion in the desired manner. In this paper, we assume that an plant parameter is varying and apply the gain-scheduling control technique design the anti-swing motion control system for the controlled plant. In this control system, the controller dynamics are adjusted in real-time according to time-varying plant parameters. And the simulation result shows that the proposed control strategy is shown to be useful to the case of time-varying system and, robust to disturbances like winds and initial sway motion.