• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.63.4-63
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• 2002

$\textbullet$ Closed System Identification for inherently unstable systems $\textbullet$ Application of Observer/controller Identification (OCID) algorithm to those systems $\textbullet$ An open-loop system model with corresponding controller and observer gains are identified using OCID $\textbullet$ Experimental example of the OCID algorithm for an inverted pendulum system operating in closed-loop $\textbullet$ Modal analysis and time response to the added distrubance are presented to evaluate the performance of the OCID algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.9
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• pp.22-29
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• 1995

An experimental method to identify the rigid properties (mass, moment of inertia, center of mass) of mounted structures is presented. A direct system identification method is developed and applied to identify the mass, damping and stiffness martix directly from the translational response of vibration testing. Conventional method is sensitive to noise since it needs artificial rotational response of temporary center of mass which is made by the linear transformation of translational response. A presented method needs only the translational response, and it is robuster to noise than conventional method. Several experimental and numerical implementations show the presented method is effective.

• 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.

• Journal of Electrical Engineering and information Science
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• v.3 no.1
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• pp.86-93
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• 1998

In this paper, we propose an effective on-line identification method for rotor resistance, which is useful in making speed control of induction motors without rotational transducers robust with respect to the variation in rotor resistance. Our identification method for rotor resistance is based on the linearly perturbed equations of the closed-loop system for sensorless speed control about th operating point. Our identification method for rotor resistance uses only the information of stator currents and voltages. In can provide fairly good identification accuracy regardless of load conditions. Some experimental results are presented to demonstrate the practical use of our identification method. For our experimental work, we have built a sensorless control system, in which all algorithms are implemented on a DSP. Our experimental results confirm that our on-line identification method allows for high precision speed control of commercially available induction motors without rotational transducers.

• Smart Structures and Systems
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• v.20 no.4
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• pp.415-426
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• 2017

Traditional experimental verification for nonlinear system identification often faces the problem of experiment model repeatability. In our research, a steel frame experimental model is developed to imitate the behavior of a single story steel frame under horizontal excitation. Two adjustable rotational dampers are used to simulate the plastic hinge effect of the damaged beam-column joint. This model is suggested as a benchmark model for nonlinear dynamics study. Since the nonlinear form provided by the damper is unknown, a Morlet wavelet based method is introduced to identify the mathematical model of this structure under different damping cases. After the model identification, earthquake excitation tests are carried out to verify the generality of the identified model. The results show the extensive applicability and effectiveness of the identification method.

• 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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.588-593
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• 2007

Fiber reinforced polymer(FRP) composite decks are new to bridge applications and hence not much literature exists on their structural mechanical behavior. As there are many differences between numerical displacements through static analysis of the primary model and experimental displacements through static load tests, system identification (SI)techniques such as Neural Networks (NN) and support vector machines (SVM) utilized in the optimization of the FE model. During the process of identification, displacements were used as input while stiffness as outputs. Through the comparison of numerical displacements after SI and experimental displacements, it can note that NN and SVM would be effective SI methods in modeling an FRP deck. Moreover, two methods such as response surface method and iteration were proposed to optimize the estimated stiffness. Finally, the results were compared through the mean square error (MSE) of the differences between numerical displacements and experimental displacements at 6 points.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.565-570
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• 2007

Fiber reinforced polymer(FRP) composite decks are new to bridge applications and hence not much literature exists on their structural mechanical behavior. As there are many differences between numerical displacements through static analysis of the primary model and experimental displacements through static load tests, system identification (SI)techniques such as Neural Networks (NN) and support vector machines (SVM) utilized in the optimization of the FE model. During the process of identification, displacements were used as input while stiffness as outputs. Through the comparison of numerical displacements after SI and experimental displacements, it can note that NN and SVM would be effective SI methods in modeling an FRP deck. Moreover, two methods such as response surface method and iteration were proposed to optimize the estimated stiffness. Finally, the results were compared through the mean square error (MSE) of the differences between numerical displacements and experimental displacements at 6 points.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.734-736
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• 1998

In this paper we obtain a discrete mathmatical model of a Gas turbine control system from experimental data. we find appropriate input signal and parameter estimation algorithm for identification of the gas turbine control system. Under these conditions experimental data are collected from real system and parameters are estimated by the recursive least square algorithm. The computer simulation results show that the proposed experimental procedure is appropriate for the identification of the gas turbine control system. The model validation is excuted by real data from the Gunsan Gas Turbine Power Plant.

• Wind and Structures
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• v.7 no.3
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• pp.187-202
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• 2004

Wind tunnel experiments are often performed for the identification of aeroelastic parameters known as flutter derivatives that are necessary for the prediction of flutter instability for flexible structures. Experimental determination of all the eighteen flutter derivatives for a section model facilitates complete understanding of the physical mechanism of flutter. However, work in the field of identifying all the eighteen flutter derivatives using section models with all three degree-of-freedom (DOF) has been limited. In the current paper, all eighteen flutter derivatives for a streamlined bridge deck and an airfoil section model were identified by using a new system identification technique, namely, Iterative Least Squares (ILS) approach. Flutter derivatives of the current bridge and the Tsurumi bridge are compared. Flutter derivatives related to the lateral DOF have been emphasized. Pseudo-steady theory for predicting some of the flutter derivatives is verified by comparing with experimental data. The three-DOF suspension system and the electromagnetic system for providing the initial conditions for free-vibration of the section model are also discussed.