• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.530-535
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• 2004

This paper considers a state set estimation (SSE) based model predictive control (MPC) for linear parameter- varying (LPV) systems with input constraint. We estimate, at each time instant, a feasible set of all states which are consistent with system model, measurements and a priori information, rather than the state itself. By combining a state-feedback MPC and an SSE, we design an SSE-based MPC algorithm that stabilizes the closed-loop system. The proposed algorithm is solved by semi-de�nite program involving linear matrix inequalities. A numerical example is included to illustrate the performance of the proposed algorithm.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.5
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• pp.625-630
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• 1986

In this paper, an adaptive observer based upon the exponentially weighted least-square method is implemented in the design of a model reference adaptive controller for an unknown time-invariant discrete single-input single-output linear plant. The adaptive observer estimates the padrameter vectors and initial state vector. The control input is determined so that the output of the plant converges to the output of the stable model reference.

• Journal of applied mathematics & informatics
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• v.18 no.1_2
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• pp.591-601
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• 2005

The results of fuzzy linear regression are very sensitive to irregular data. When this points exist in a set of data, a fuzzy linear regression model can be incorrectly interpreted. The purpose of this paper is to detect irregular data and to propose robust fuzzy linear regression based on M-estimators with triangular fuzzy regression coefficients for crisp input-output data. Numerical example shows that irregular data can be detected by using the residuals based on M-estimators, and the proposed robust fuzzy linear regression is very resistant to this points.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.5
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• pp.111-117
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• 2003

The Taylor model concept is introduced to design a controller with input and output data only. The parameters in Taylor model can be estimated using the input and output data and a controller can be designed based on Taylor model. The accuracy of Taylor model approximation can be improved by increasing the observation window and the order of Taylor model. The LQR method is applied to Taylor model to design power system stabilizers (PSS), and compared with the conventional PSS.

• Water Engineering Research
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• v.4 no.1
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• pp.1-17
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• 2003

A mathematical model was developed to simulate the performance of a prototype wind-powered reverse osmosis desalination system. The model consists of two sub-models operated in a series. The first sub-model is the wind-energy conversion sub-model, which has wind energy and feed water as its input and pressurized feed water as its output. The second sub-model is a reverse osmosis (RO) process sub-model, with pressurized feed water as its input and the flow and salinity of the product water or permeate as its output. Model coefficients were determined based on field experiments of a prototype wind powered RO desalination system of the University of Hawaii, from June to December 2001. The mathematical model developed by this study predicts the performance of wind-powered RO desalination systems under different design conditions. The system optimization is achieved using a linear programming approach. Based on the results of system optimization, a design guide is prepared, which can be used by both manufacturer and end-user of the wind-driven reverse osmosis system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.1
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• pp.70-77
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• 2001

This paper present a fuzzy mxlel of describing reacton features for ozon contactor in advanced water treatn-ent systems. Input and output variables are chosen by considenng the object of ozon processing and several parameters related to management of water quahty. Dissolved organic carbon concentration, $UV_{254}$ absorptIon and $KM_NO_4$ consumption are proposed as common variables in input and outp.lt variables. Furthermore own concentration, raw water's temperature and contact time are suggested as input variables, Membership hmctions for input variables have triangular type share and the grades in each lrembership function are determined by investigating process data gathered at pilot planl The decision parts of fuzzy model have linear combination form of input variables and coefficients included in such linear equations are computedd with process clata in the sense of least square error Also fuzzy trodel suggested in this paper is partitioned by 3 independent fuzzy rnxlels using the characteristics of having no interactions armng output variables. As a result, such fuzzy mxlel has rrerits in computation and comprehension. According to simulatIon results, fuzzy moIel's outputs give almost similar data to process output under same input conditions.

• Water for future
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• v.24 no.3
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• pp.61-70
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• 1991

the relationships between rainfall and runoff are analyzed statistically and modelled using discrete linear transfer function, which can be shown with the relations between input and output in hydrologic system. The procedures of identification, estimation and diagnostic checking of model are proposed, and the suitabilith of assume model is determined by the statistics used in time series analysis.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2393-2398
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• 2003

In this paper, we proposes a new fuzzy inference system for modeling nonlinear systems given input and output data. In the suggested fuzzy inference system, the number of fuzzy rules and parameter values of membership functions are automatically decided by using the kernel-based method. The kernel-based method individually performs linear transformation and kernel mapping. Linear transformation projects input space into linearly transformed input space. Kernel mapping projects linearly transformed input space into high dimensional feature space. The structure of the proposed fuzzy inference system is equal to a Takagi-Sugeno fuzzy model whose input variables are weighted linear combinations of input variables. In addition, the number of fuzzy rules can be reduced under the condition of optimizing a given criterion by adjusting linear transformation matrix and parameter values of kernel functions using the gradient descent method. Once a structure is selected, coefficients in consequent part are determined by the least square method. Simulated result illustrates the effectiveness of the proposed technique.

• Journal of the Korean Magnetics Society
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• v.14 no.6
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• pp.232-235
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• 2004

Linear GMR isolator which is profitable for transmitting analog signal was modeled and the output voltage and current in relation to the input current were investigated. GMR isolator modeling was divided into two parts, namely magnetic and electric parts. The flow chart of the modeling was drawn in which the MR curve of the spin valves were incorporated to obtain the electrical voltage output. For magnetic modeling, 3-dimensional model of planar coil was analyzed by FEM method to obtain the magnetic field strength corresponding to the input current. Coil efficiency of the planar coil having magnetic core layer was shown to have about 1.5 times larger than that of the coil without the magnetic core layer. The feedback coil current(output current) corresponding to the input coil current was calculated to be within ${\pm}$0.25 mA of the linear fitting function of I$\_$out/= I$\_$in/-5 mA. Also, the response time and output waveforms were obtained when the coil current was a rectangular waveform. The rise time and fall time was 6 ${\mu}\textrm{s}$, respectively when the slew rate of the op-amp was 0.3 V/${\mu}\textrm{s}$.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.570-575
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• 2012

In a wireless communication RF transmitter, the output of a quadrature modulator (QM) is distorted by not only the linear imperfection features such as in/quadrature-phase (I/Q) input gain imbalance, local phase imbalance, and local gain imbalance but also the nonlinear imperfection features such as direct current (DC) offset and mixer nonlinearity related to in-band spurious signal. In this paper, we propose the unified QM model to analyze the combined effects of the linear and nonlinear imperfection features on the performance of the QM. The unified QM model consists of two identical nonlinear systems and modified I/Q inputs based on the two-port nonlinear mixer model. The unified QM model shows that the output signals can be expressed by mixer circuit parameters such as intercept point and gain as well as the imperfection features. The proposed approach is validated by not only simulation but also measurement.