• 대한전기학회논문지:시스템및제어부문D
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• 제51권3호
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• pp.89-95
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• 2002

This paper describes a new identification method for friction in motion control systems, in which the friction model is not necessarily linear in parameters. The proposed method works well with any measurement data of velocity and input control force, as long as the initial and final velocities are identical. Most importantly, the proposed method does not require the information of acceleration for its implementation, in contrast with the previously known methods. This is due to the orthogonality property between acceleration and a function of velocity. In particular, if the parametric model is linear in parameters, its friction parameters can be identified in closed form without resorting to numerical search methods. To illuminate further the generality and practicality of the proposed friction identification method, we show good performance of the proposed method through some simulation results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 B
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• pp.703-705
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• 1999

In this paper, we introduce the direction of arrival(DOA) estimation of distributed signal based on the improved ESPRIT algorithm. Most research on the estimation of DOA has been performed based on the assumption that the signal sources are point sources. However, we consider a two-dimensional distributed signal source model using improved ESPRIT algorithm. In the distributed signal source model, a source is represented by two parameters, the azimuth angle and elevation angle. We address the estimation of the elevation and azimuth angles of distributed sources based on the parametric source modeling in the three-dimensional space with two uniform linear arrays. The array output vector is obtained by integrating a steering vector over all direction of arrival with the weighting of a distributed source density function. We also develop an efficient estimation procedures that can reduce the computational complexity. Some examples are shown to demonstrate explicity the estimation procedures under the distributed signal source model.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.2097-2098
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• 2006

In this study, we introduce the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA). HFCGA is a kind of multi-populations of Parallel Genetic Algorithms(PGA), and it is used for structure optimization and parameter identification of fuzzy set model. It concerns the fuzzy model-related parameters as the number of input variables, a collection of specific subset of input variables, the number of membership functions, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.988-991
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• 1996

The purpose of this thesis is to develop methods of designing robust LQR/LQG controllers for time-varying systems with real parametric uncertainties. Controller design that meet desired performance and robust specifications is one of the most important unsolved problems in control engineering. We propose a new framework to solve these problems using Linear Matrix Inequalities (LMls) which have gained much attention in recent years, for their computational tractability and usefulness in control engineering. In Robust LQR case, the formulation of LMI based problem is straightforward and we can say that the obtained solution is the global optimum because the transformed problem is convex. In Robust LQG case, the formulation is difficult because the objective function and constraint are all nonlinear, therefore these are not treatable directly by LMI. We propose a sequential solving method which consist of a block-diagonal approach and a full-block approach. Block-diagonal approach gives a conservative solution and it is used as a initial guess for a full-block approach. In full-block approach two LMIs are solved sequentially in iterative manner. Because this algorithm must be solved iteratively, the obtained solution may not be globally optimal.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2001년도 춘계학술대회 논문집
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• pp.51-57
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• 2001

In order to estimate attenuation characteristics, nonparametric model is used. Evaluation of this model requires suites of average spectral amplitude data as a function of distance without adopting any assumed parametric forms. Values of Quality factor, Q were estimated for individual frequencies by matrix inversion of S wave amplitude spectra for 21 records of 8 small and intermediate earthquakes (2.7$(1/r)^{1/2}$. The best models for 1/Q values were found to be 0.000453+0.001851/f and 0.000451+0.009261/f, respectively. This results can be compared to the conventional functional form of Q=$af^{b}$ and be used to estimate source spectra and site response.

• Steel and Composite Structures
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• 제4권1호
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• pp.9-21
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• 2004

The present study aims at the reliability analysis of steel towers against the limit state of deflection. For this purpose tip deflection of the tower has been obtained after carrying out the dynamic analysis of the tower using modal method. This tip deflection is employed for subsequent reliability analysis. A limit state function based on serviceability criterion of deflection is derived in terms of random variables. A complete procedure of reliability computation is then presented. To study the influence of various random variables on tower reliability, sensitivity analysis has been carried out. Design points, important for probabilistic design of towers, are also located on the failure surface. Some parametric studies have also been included to obtain the results of academic and field interest.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1211-1214
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• 1999

Power system operating conditions vary with system configuration and loading conditions. Coefficients in nominal system model change in a complex manner with different operating point and so does system dynamic behavior. With the aid of unstructured and structured uncertainty descriptions the worst system variations can be estimated and formulated into two different uncertainty models multiplicative unstructured uncertainty in the form of transfer function and structured uncertainty with the parametric uncertainty description. in frequency domain

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.305-307
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• 1992

This paper presents a technique, which identifies both process noise covariance and sensor noise covariance by using innovation correlation test. A correlation test, which checks whether the square root Kalman filter is workingly optimal or not, is given. The system is stochastic autoregressive moving-average model with auxiliary white noise Input. The linear quadratic Gaussian control is used for minimizing stochastic cost function. This paper indentifies Q, R, and estimates parametric matrics $A(q^{-1}),B(q^{-1}),C(q^{-1})$ by means of extended recursive least squares and model reference control. And The proposed technique has been validated in simulation results on the fourth order system.

• 제어로봇시스템학회논문지
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• 제10권4호
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• pp.295-303
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• 2004

Previously it has been shown that the all pole systems resulting good time responses can be characterized by so called K-polynomial. The polynomial is defined in terms of the principal characteristic ratio $\alpha_1$ and the generalized time constant $\tau$ . In this paper, Part II presents several sensitivity analyses of such systems with respect to $\alpha_1$ and $\tau$ changes. We first deal with the root sensitivity to the perturbation of $\alpha_1$ . By way of determining the unnormalized function sensitivity, both time response sensitivity and frequency response sensitivity are derived. Finally, the root sensitivity relative to $\tau$ change is also analyzed. These results provide some useful insight and background theory when we select of and l to compose a reference model of which denominator is a K-polynomial, which is illustrated by examples.

• 전자공학회논문지C
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• 제34C권11호
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• pp.95-103
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• 1997

A robust center estimation tecnique of n-fold engineering parts is presented, which use self-organizing neural networks with generating and merging learning for training neural units. To estimate the center of the n-fold engineering parts using neural networks, the segmented boundaries of the interested part are approximated to strainght lines, and the temporal estimated centers by thecosine theorem which formed between the approximaged straight line and the reference point, , are indexed as (.sigma.-.theta.) parameteric vecstors. Then the entries of parametric vectors are fed into self-organizing nerual network. Finally, the center of the n-fold part is extracted by mean of generating and merging learning of the neurons. To accelerate the learning process, neural network uses an adaptive learning rate function to the merging process and a self-adjusting activation to generating process. Simulation results show that the centers of n-fold engineering parts are effectively estimated by proposed technique, though not knowing the error distribution of estimated centers and having less information of boundaries.