• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.370-372
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• 1986

In this paper we propose a Generalized Minimum Variance Self-tuning Control of the system with an autoregressive noise model. To establish a Generalized Minimum Variance Control, the control input is also included in a cost function and a novel identity is introduced. The effectiveness of this algorithm is demonstrated by the computer simulation.

• Communications for Statistical Applications and Methods
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• v.23 no.3
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• pp.251-258
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• 2016

It is important to verify the identity of plant-based multivitamins prepared with a natural-concept and popular for daily consumption because they are easily purchased in markets with imperfect information. For this study, a generalized partially double-index model (GPDIM) was employed as a main statistical method to identify the contribution of vitamins and phytochemicals to antioxidant potentials using data on antioxidant capacities and chemical fingerprinting. A bootstrapping approach via sufficient dimension reduction is adopted to estimate the two unknown coefficient vectors in the GPDIM. Fifth order polynomial regressions are fitted to measure the contributions of vitamins and phytochemicals after estimating the coefficient vectors with the two double indices.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.3
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• pp.388-394
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• 2003

Hydraulic system is difficult to obtain a suitable performance due to the nonlinearity load pressure change and system parameter variation. The requirement of control a1gorithm has been complex in order to satisfy the performance. The adaptive control is a control method which is suggested to achieve the control object under the plant characteristics change. In spite of the case that plant characteristics and the degree of variation are difficult to grasp. the adaptive control could keep the characteristics of closed-loop system generally. In this study. a method of combined generalized minimum variance adaptive control (GMVAC) and output error feedback is proposed, in order to solve the problem of non-minimum phase of plant and the vibration and overshoot in initial response. The control performance according to the variation of characteristics of plant is evaluated by changing the supply pressure. The experimental results show the effectiveness of the proposed scheme.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.663-668
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• 1990

This paper deals with the application of a Generalized Predictive Control (CPC) to a Pressurized Water Reactor (P.W.R) Nuclear Power Plant. Generalized Predictive Control is a sort of Explicit Self-Tuning Control. Current self-tuning algorithms lack robustness to prior choices of either dead-time (input time delay of a plant) or model order. GPC is shown by simulation studies to be superior to accepted self-tuning techniques such as minimum variance and pole-placement from the viewpoint that it is robust to prior choices of dead-time or model order. In this paper a GPC controller is designed to control the P.W.R. nuclear power rlant with varying dead-time and through the designing procedure the designer is free from the constraint of knowing the exact dead-time. The controller is constructed based on the 2nd order linear model approximated in the vicinity of operating point. To ensure that this low-order model describes the complex real dynamics well enough for control purposes, model parameters are updated on-line with a Recursive Least Squares algorithm. Simulation results are successful and show the possibilities of the GPC control application to actual plants with varying or unknown dead-time.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.1
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• pp.124-132
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• 2004

A hydraulic system is difficult to keep the performance due to non-linearity, load pressure which changes according to working condition and system parameter variation, the requirement of control algorithm has been risen in order to satisfy them. An adaptive control is a control method which is suggested to achieve a control object though plant characteristics change. In spite of the case that plant characteristics and the degree of variation are difficult to grasp, adaptive control can keep the characteristics of closed-loop system regularly. In this study GMVAC(generalized minimum variance adaptive control) combined with output error feedback is proposed in order to solve problems of non-minimum phase, vibration and overshoot in initial response of the plant. The control performance according to the variation of characteristics of the plant is evaluated by changing the supply pressure only.

• Journal of Electrical Engineering and Technology
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• v.6 no.5
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• pp.706-712
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• 2011

Necessary and sufficient conditions for the existence of diagonal, block-diagonal, and triangular decoupling controllers in linear multivariable systems for the most general setting are presented. The plant model in this study is sufficiently general to accommodate non-square plant and non-unity feedback cases with one-degree-of-freedom (1DOF) or two-degree-of-freedom (2DOF) controller configuration. The existence condition is described in terms of rank conditions on the coefficient matrices in partial fraction expansions.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2093-2098
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• 2005

In this paper, $H_{2}$ designs for the one-degree-of-freedom decoupling control systems are treated for the generalized plant. The optimal $H_{2}$ controller is obtained together with the ones that yield finite $H_{2}$ cost functions under compact assumptions. It is shown that the optimal closed transfer matrix is strictly proper under the reasonable order assumptions on the generalized plant.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.272-279
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• 1997

In this paper, a fuzzy generalized predictive control (FGPC) for non-linear plants is proposed. In the proposed method, the receding horizon control is applied to the control part, while fuzzy systems are used for the predictor part. It is suggested that the fuzzy predictor is time-varying affine with respect to input variables for easy computation of control inputs. Since the receding horizon control can be obtained only with a predictor instead of a plant model, the fuzzy predictor is obtained directly from input-output data without identifying a plant model. A parameter estimation algorithm is used for identifying the fuzzy predictor. The control inputs of the FGPC are computed by minimizing a receding horizon cost function with predicted plant outputs. The proposed controller has a similar architecture to the generalized predictive control (GPC) except for the predictor synthesis method, and thus may possess inherent good properties of the GPC. Computer simulations show that the performance of the FGPC is satisfactory.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.7A
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• pp.967-977
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• 2000

In this paper, we apply a mixed $H_2/H_{\infty}$ control to a generalized plant of inverted pendulum system represented by an LFR(Linear Fractional Representation). First, in order to obtain the generalized plant, the linear model of the inverted pendulum represented by an LFR(Linear fractional Representation) is derived. In LFR, we consider system uncertainties as three nonlinear components and a pendulum mass uncertainty. Augmenting the LFR model by adding weighting functions, we get a generalized plant. And then, we design a mixed $H_2/H_{\infty}$ controller for the generalized plant. In order to design the mixed $H_2/H_{\infty}$ controller, we use the LMI technique. To evaluate control performances and robust stability of the mixed $H_2/H_{\infty}$ controller designed, we compare it with the $H_{\infty}$ controller through the simulation and experiment. In the result, with the fewer feedback information, the mixed $H_2/H_{\infty}$ controller shows the better control performances and robust stability than the $H_{\infty}$ controller in the sense of pendulum angle.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.1
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• pp.84-97
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• 2020

This paper aims to develop an optimized inventory management method for safety critical Instrument and Control (I&C) components. In this regard, the paper focuses on estimating the consumption rate of I&C components using demand forecasting methods. The target component for this paper is the Foxboro SPEC-200 controller. This component was chosen because it has highest consumption rate among the safety critical I&C components in Korean OPR-1000 NPPs. Three analytical methods were chosen in order to develop the demand forecasting methods; Poisson, Generalized Linear Model (GLM) and Bootstrapping. The results show that the GLM gives better accuracy than the other analytical methods. This is because the GLM considers the maintenance level of the component by discriminating between corrective and preventive.