• Title/Summary/Keyword: linear system

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A Simultaneous Perturbation Stochastic Approximation (SPSA)-Based Model Approximation and its Application for Power System Stabilizers

  • Ko, Hee-Sang;Lee, Kwang-Y.;Kim, Ho-Chan
    • International Journal of Control, Automation, and Systems
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    • v.6 no.4
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    • pp.506-514
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    • 2008
  • This paper presents an intelligent model; named as free model, approach for a closed-loop system identification using input and output data and its application to design a power system stabilizer (PSS). The free model concept is introduced as an alternative intelligent system technique to design a controller for such dynamic system, which is complex, difficult to know, or unknown, with input and output data only, and it does not require the detail knowledge of mathematical model for the system. In the free model, the data used has incremental forms using backward difference operators. The parameters of the free model can be obtained by simultaneous perturbation stochastic approximation (SPSA) method. A linear transformation is introduced to convert the free model into a linear model so that a conventional linear controller design method can be applied. In this paper, the feasibility of the proposed method is demonstrated in a one-machine infinite bus power system. The linear quadratic regulator (LQR) method is applied to the free model to design a PSS for the system, and compared with the conventional PSS. The proposed SPSA-based LQR controller is robust in different loading conditions and system failures such as the outage of a major transmission line or a three phase to ground fault which causes the change of the system structure.

A NOTE ON PARTIAL SIGN-SOLVABILITY

  • Hwang, Suk-Geun;Park, Jin-Woo
    • Bulletin of the Korean Mathematical Society
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    • v.43 no.3
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    • pp.471-478
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    • 2006
  • In this paper we prove that if AX=b is a partial sign-solvable linear system with A being sign non-singular matrix and if ${\alpha}=\{j:\;x_j\;is\;sign-determined\;by\; Ax=b\}, then $A_{\alpha}X_{\alpha}=b_{\alpha}$ is a sign-solvable linear system, where $A_{\alpha}$ denotes the submatrix of A occupying rows and columns in o and xo and be are subvectors of x and b whose components lie in ${\alpha}$. For a sign non-singular matrix A, let $A_l,\;...,A_{\kappa}$ be the fully indecomposable components of A and let ${\alpha}_i$ denote the set of row numbers of $A_r,\;r=1,\;...,\;k$. We also show that if $A_x=b$ is a partial sign-solvable linear system, then, for $r=1,\;...,\;k$, if one of the components of xor is a fixed zero solution of Ax=b, then so are all the components of x_{{\alpha}r}$.

A Study on the State Space Identification Model of the Dynamic System using Neural Networks (신경회로망을 이용한 동적 시스템의 상태 공간 인식 모델에 관한 연구)

  • 이재현;강성인;이상배
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1997.10a
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    • pp.115-120
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    • 1997
  • System identification is the task of inferring a mathematical description of a dynamic system from a series of measurements of the system. There are several motives for establishing mathematical descriptions of dynamic systems. Typical applications encompass simulation, prediction, fault diagnostics, and control system design. The paper demonstrates that neural networks can be used effective for the identification of nonlinear dynamical systems. The content of this paper concerns dynamic neural network models, where not all inputs to and outputs from the networks are measurable. Only one model type is treated, the well-known Innovation State Space model(Kalman Predictor). The identification is based only on input/output measurements, so in fact a non-linear Extended Kalman Filter problem is solved. Even for linear models this is a non-linear problem without any assurance of convergence, and in spite of this fact an attempt is made to apply the principles from linear models, an extend them to non-linear models. Computer simulation results reveal that the identification scheme suggested are practically feasible.

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A note on a method for estimating the linear expenditure system with one restriction

  • Lee, Seok-Koo
    • Journal of the Korean Statistical Society
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    • v.4 no.1
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    • pp.67-78
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    • 1975
  • Over twenty-five years ago, Professor Klein and Rubin presented the linear expenditure system. That system was first estimated by Stone. Subsequently many investigators have estimated that system. In this paper, many points of the error structure shown by Pollak and Wales are referred to. Barten presented an estimation theorem on a singular covariance matrix. In order to estimate parameters, we place an emphasis on the maximum likihood method which we believe to be most appropriate. As we have one linear restriction on parameters to be estimated, we maximized the associated likelihood function subject to that linear restriction through the well-known lagrange multiplier method. This paper is organized in the following fashion : (1) a brief description on classical consumer theory, (2) a linear expenditure system and its constraint, (3) dyanmic specification and stochastic specification, (4) estimation method, and (5) conclusion.

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Static equilibrium and linear vibration analysis of a high speed electric train system (고속전철 시스템의 정적평형 및 선형진동 해석)

  • 김종인;유홍희;황요하
    • Journal of the Korean Society for Railway
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    • v.2 no.4
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    • pp.1-8
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    • 1999
  • A formulation to perform static equilibrium and linear vibration analysis is presented in this paper. The formulation employs minimum number of equations of motion which are derived by using a partial velocity matrix. The static equilibrium analysis is performed first, then the linear vibration analysis is performed at the static equilibrium position. By using the formulation presented in this paper, static equilibrium and linear vibration analysis of a high speed electric train system are performed. A single bogie system, a power car vehicle, and a train system which consists of five vehicles are analyzed, respectively. Natural frequencies and a few lowest mode shapes of the two are identified in this paper.

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Approximate Response of a Non-linear Vibration Isolation System Using the Harmonic Balance Method (하모닉 밸런스법을 이용한 비선형 진동절연 시스템의 근사적 응답)

  • Lee, Gun-Myung
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.17 no.6
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    • pp.124-129
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    • 2018
  • A non-linear vibration isolation system which is composed of a non-linear spring and a linear damper was proposed in past research. When the support of the isolation system is excited harmonically, the response component of the isolation system mass at the excitation frequency has been calculated approximately using the harmonic balance method. The response was approximated by a single mode, and the result was compared with a numerical result which is assumed as an accurate one. Next, the response was approximated by two modes, and the result was compared with the former one.

Design of Linear Recursive Target State Estimator for Collision Avoidance System (차량 충돌 방지 시스템을 위한 선형 순환 표적 추정기 설계)

  • Han, Seul-Ki;Ra, Won-Sang;Whang, Ick-Ho;Park, Jin-Bae
    • Proceedings of the KIEE Conference
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    • 2011.07a
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    • pp.1740-1741
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    • 2011
  • This paper proposes a new linear recursive target state estimator for automotive collision warning system. The target motion is modeled in Cartesian coordinate system while the radar measurements such as range, line-of-sight angle and range rate are obtained in polar coordinate system. To solve the problem by nonlinear relation between these two coordinate system, a practical linear filter design scheme employing the predicted line-of-sight Cartesian coordinate system (PLCCS) is proposed. Especially, PLCCS can effectively incorporate range rate measurements into target tracking system. It is known that the utilization of range rate measurements enables the improvement of target tracking performance. Moreover, PLCCS based target tracking system is implemented by linear recursive filter structure and hence is more suitable scheme for the development of reliable collision warning system. The performance of the proposed method is demonstrated by computer simulations.

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Use of Higher Order Frequency Response Functions for Non-Linear Parameter Estimation (고차 주파수응답함수를 이용한 비선형시스템의 매개변수 추정)

  • 이건명
    • Journal of KSNVE
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    • v.7 no.2
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    • pp.223-229
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    • 1997
  • Presented is a method to estimate system parameters of a system with polynomial non-linerities from the measured higher order frequency response functions. Higher order FRFs can be measured on some restricted regions by sinusoidally exciting a non-linear system with various input amplitudes and measuring the response component at the excitation frequency. These higher order FRFs can be expressed in terms of system parameter, and the system parameters can be estimated from the measured FRFs. Since the expressions for higher order FRFs are complicated, system parameters can be estimated from them using an optimization technique. The present method has been applied to a simulated single degree of freedom system with non-linear stiffness and damping, and has estimated accurate system parameters.

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An improved linear sampled-data output regulators (개선된 선형 샘플치 출력 조절기)

  • 정선태
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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    • pp.1726-1729
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    • 1997
  • In general, the solvability of linear robust output regulation problem are not preserved under time-sampling. Thus, it is found that the digital regulator implemented by itme-sampling of anlog output regulator designed based on the continuous-time linear system model is nothing but a 1st order approximation with respect to time-sampling. By the way, one can design an improved sampled-data regulator with respect to sampling time by utilizing the intrinsic structure of the system. In this paper, we study the system structures which it is possible to design an improved sampled-data regulator with respect to sampling time.

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SPLITTING METHOD OF DENSE COLUMNS IN SPARSE LINEAR SYSTEMS AND ITS IMPLEMENTATION

  • Oh, Seyoung;Kwon, Sun Joo
    • Journal of the Chungcheong Mathematical Society
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    • v.10 no.1
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    • pp.147-159
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    • 1997
  • It is important to solve the large sparse linear system appeared in many application field such as $AA^Ty={\beta}$ efficiently. In solving this linear system, the sparse solver using the splitting method for the relatively dense column is experimentally better than the direct solver using the Cholesky method.

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