• Title, Summary, Keyword: System identification

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On-line System Identification using State Observer

  • Park, Duck-Gee;Hong, Suk-Kyo
    • 제어로봇시스템학회:학술대회논문집
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    • pp.2538-2541
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    • 2005
  • This paper deals one of the methods of system identification, especially on-line system identification in time-domain. The algorithm in this study needs all states of the system as well input to it for system identification. In this reason, Kalman filter is used for state estimation. But in order to implement a state estimator, the fact that a system model must be known is logical contradiction. To overcome this, state estimation and system parameter estimation are performed simultaneously in one sample. And the result of the system parameter estimation is used as basis to state estimation in next sample. On-line system identification comes, in every sample by performing both processes of state estimation and parameter estimation that are related mutually and recursively. This paper demonstrates the validity of proposed algorithm through an example of an unstable inverted pendulum system. This algorithm can be useful for on-line system identification of a system that has fewer number of measurable output than system order or number of states.

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Two-Phase Neuro-System Identification Based on Artificial System (모조 시스템 형성에 기반한 2단계 뉴로 시스템 인식)

  • 배재호;왕지남
    • Journal of the Korean Society for Precision Engineering
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    • v.15 no.3
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    • pp.107-118
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    • 1998
  • Two-phase neuro-system identification method is presented. The 1$^{st}$-phase identification uses conventional neural network mapping for modeling an input-output system. The 2$^{nd}$ -phase modeling is also performed sequentially using the 1$^{st}$-phase modeling errors. In the 2$^{nd}$ a phase modeling, newly generated input signals, which are obtained by summing the 1st-phase modeling error and artificially generated uniform series, are utilized as system's I-O mapping elements. The 1$^{st}$-phase identification is interpreted as a “Real Model” system identification because it uses system's real data(i.e., observations and control inputs) while the 2$^{nd}$ -phase identification as a “Artificial Model” identification because of using artificial data. Experimental results are given to verify that the two-phase neuro-system identification could reduce the overall modeling errors.rrors.

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A Study on the ALS Method of System Identification (시스템동정의 ALS법에 관한 연구)

  • Lee, D.C.
    • Journal of the Korea Society For Power System Engineering
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    • v.7 no.1
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    • pp.74-81
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    • 2003
  • A system identification is to estimate the mathematical model on the base of input output data and to measure the output in the presence of adequate input for the controlled system. In the traditional system control field, most identification problems have been thought as estimating the unknown modeling parameters on the assumption that the model structures are fixed. In the system identification, it is possible to estimate the true parameter values by the adjusted least squares method in the input output case of no observed noise, and it is possible to estimate the true parameter values by the total least squares method in the input output case with the observed noise. We suggest the adjusted least squares method as a consistent estimation method in the system identification in the case where there is observed noise only in the output. In this paper the adjusted least squares method has been developed from the least squares method and the efficiency of the estimating results was confirmed by the generating data with the computer simulations.

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System identification of a building structure using wireless MEMS and PZT sensors

  • Kim, Hongjin;Kim, Whajung;Kim, Boung-Yong;Hwang, Jae-Seung
    • Structural Engineering and Mechanics
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    • v.30 no.2
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    • pp.191-209
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    • 2008
  • A structural monitoring system based on cheap and wireless monitoring system is investigated in this paper. Due to low-cost and low power consumption, micro-electro-mechanical system (MEMS) is suitable for wireless monitoring and the use of MEMS and wireless communication can reduce system cost and simplify the installation for structural health monitoring. For system identification using wireless MEMS, a finite element (FE) model updating method through correlation with the initial analytical model of the structure to the measured one is used. The system identification using wireless MEMS is evaluated experimentally using a three storey frame model. Identification results are compared to ones using data measured from traditional accelerometers and results indicate that the system identification using wireless MEMS estimates system parameters with reasonable accuracy. Another smart sensor considered in this paper for structural health monitoring is Lead Zirconate Titanate (PZT) which is a type of piezoelectric material. PZT patches have been applied for the health monitoring of structures owing to their simultaneous sensing/actuating capability. In this paper, the system identification for building structures by using PZT patches functioning as sensor only is presented. The FE model updating method is applied with the experimental data obtained using PZT patches, and the results are compared to ones obtained using wireless MEMS system. Results indicate that sensing by PZT patches yields reliable system identification results even though limited information is available.

System identification of high-rise buildings using shear-bending model and ARX model: Experimental investigation

  • Fujita, Kohei;Ikeda, Ayumi;Shirono, Minami;Takewaki, Izuru
    • Earthquakes and Structures
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    • v.8 no.4
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    • pp.843-857
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    • 2015
  • System identification is regarded as the most basic technique for structural health monitoring to evaluate structural integrity. Although many system identification techniques extracting mode information (e.g., mode frequency and mode shape) have been proposed so far, it is also desired to identify physical parameters (e.g., stiffness and damping). As for high-rise buildings subjected to long-period ground motions, system identification for evaluating only the shear stiffness based on a shear model does not seem to be an appropriate solution to the system identification problem due to the influence of overall bending response. In this paper, a system identification algorithm using a shear-bending model developed in the previous paper is revised to identify both shear and bending stiffnesses. In this algorithm, an ARX (Auto-Regressive eXogenous) model corresponding to the transfer function for interstory accelerations is applied for identifying physical parameters. For the experimental verification of the proposed system identification framework, vibration tests for a 3-story steel mini-structure are conducted. The test structure is specifically designed to measure horizontal accelerations including both shear and bending responses. In order to obtain reliable results, system identification theories for two different inputs are investigated; (a) base input motion by a modal shaker, (b) unknown forced input on the top floor.

System identification method for the auto-tuning of power plant control system with time delay (시간지연을 가진 발전소 제어시스템의 자동동조를 위한 System identification 방법)

  • 윤명현;신창훈;박익수
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1008-1011
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    • 1996
  • Most control systems of power plants are using classical PID controllers for their process control. In order to get the desired control performances, the correct tuning of PID controllers is very important. Sometimes, it is necessary to retune PID controllers after the change of system operating condition and system design change, etc. Commercial auto-tuning controllers such as relay feedback controller can be used for this purpose. However, using these controllers to the safety-critical systems of nuclear power plants may be cause of unsafe operation, because they are using test signals for tuning. A new system identification auto-tuning method without using test signal has been developed in this paper. This method uses process input/output signals for system identification of unknown control process. From the model information of control process which was obtained from system identification approach, the optimal PID parameters can be calculated. The method can be used in the safety-critical systems because it is not using test signals during system modeling process.

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A Study on the AR Identification of unknown system using Cumulant (Cumulant를 이용한 미지 시스템의 AR 식별에 관한 연구)

  • Lim, Seung-Gag
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.43 no.2
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    • pp.39-43
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    • 2006
  • This paper deals with the AR Identification of unknown system using cumulant, which is the 3rd order statistics of output signal in the presence of the noise signal. The algorithms for identification of unknown system we applies to the AR identification method using the cumulant which is possible to the guarantees of global convergence and the representation of amplitude and phase information of system among with the method of parametric modeling. In the process of identification, we considered unknown system to the one of AR system. After the generation of input signal, it was being passed through the system then We use the its output signal that the noise is added. As a result of identification of AR system by changing the signal to noise ratio, we get the fairly good results compared to original system output values and confirmed that the pole was located in the unit circle of z transform.

Management Policy of Identification System based on URN (정보자원관리를 위한 식별체계 구조 및 운영 정책 분석에 관한 연구)

  • Ham, Jeong-Hun;O, Sang-Hun;Kim, Seok-Hun
    • 한국디지털정책학회:학술대회논문집
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    • pp.179-193
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    • 2004
  • Introduction of domestic digital content identification system mean to building district identification by supplying international identification system and interoperability that reflect domestic special state than accept it as it is. This study presented an policy direction for the domestic digital content identification system by the results that analyzed of the identification systems that were commonly used internationally. An identification system is composed to a registration authority, registration agencies, registrants. There should be defined their roles and set up their cooperative system to be able to bring efficiency into more.

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Development of Individual Electronic Identification System Using the Inductive Transmission Method for Stockbreeding Management (개체관리를 위한 인덕터 전송방식의 개체인식 시스템 개발)

  • Ko, S.C.;Han, B.S.;Lee, J.;Kim, Y.J.;Lee, S.Y.
    • Journal of Biosystems Engineering
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    • v.27 no.5
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    • pp.451-458
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    • 2002
  • This paper introduces that livestock can be effectively managed by an individual electronic identification system. The proposed system was composed of the original code transmitter, receiver, personal computer, micro-processor, and RS485 telecommunications module. In the individual electronic identification system, the signal including encoded information of a milk-cow was transmitted from an original code transmitter to a micro-processor through RS485 telecommunications module. The transmitted signal can be successfully displayed in a personal computer. This system can be managed by 9999 individuals with a original code transmitter. The recognition rate of an individual electronic identification system was 98.5% and also auto-feeder operates very well. an individual electronic identification system was developed for automation of stockbreeding management. To automate the breeding management, it is necessary to obtain and analyze the individual information distinguished from others preferentially.

A Study on the System Identification based on Neural Network for Modeling of 5.1. Engines (S.I. 엔진 모델링을 위한 신경회로망 기반의 시스템 식별에 관한 연구)

  • 윤마루;박승범;선우명호;이승종
    • Transactions of the Korean Society of Automotive Engineers
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    • v.10 no.5
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    • pp.29-34
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    • 2002
  • This study presents the process of the continuous-time system identification for unknown nonlinear systems. The Radial Basis Function(RBF) error filtering identification model is introduced at first. This identification scheme includes RBF network to approximate unknown function of nonlinear system which is structured by affine form. The neural network is trained by the adaptive law based on Lyapunov synthesis method. The identification scheme is applied to engine and the performance of RBF error filtering Identification model is verified by the simulation with a three-state engine model. The simulation results have revealed that the values of the estimated function show favorable agreement with the real values of the engine model. The introduced identification scheme can be effectively applied to model-based nonlinear control.