• Journal of 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.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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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.

• Smart Structures and Systems
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• v.29 no.5
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• pp.715-728
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• 2022

Recently, numbers of long span pedestrian suspension bridges have been constructed worldwide. While recent tragedies regarding pedestrian suspension bridges have shown how these bridges can wreak havoc on the society, there are no specific guidelines for construction standards nor safety inspections yet. Therefore, a structural health monitoring system that could help ensure the safety of pedestrian suspension bridges are needed. System identification is one of the popular applications for structural health monitoring method, which estimates the dynamic system. Most of the system identification methods for bridges are currently adapting output-only system identification method, which assumes the dynamic load to be a white noise due to the difficulty of measuring the dynamic load. In the case of pedestrian suspension bridges, the pedestrian load is within specific frequency range, resulting in large errors when using the output-only system identification method. Therefore, this study aims to develop a system identification method for pedestrian suspension bridges considering both input and output of the dynamic system. This study estimates the location and the magnitude of the pedestrian load, as well as the dynamic response of the pedestrian bridges by utilizing artificial intelligence and computer vision techniques. A simulation-based validation test was conducted to verify the performance of the proposed system. The proposed method is expected to improve the accuracy and the efficiency of the current inspection and monitoring systems for pedestrian suspension bridges.

• Journal of Ocean Engineering and Technology
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• v.16 no.6
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• pp.25-31
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• 2002

A system identification method is introduced to increase the prediction accuracy of a ship's maneuverability in PMM test, analysis. To improve the accuracy of linear hydrodynamic coefficients, the analysis techniques of pure sway and yaw tests are developed, and confirmed. In the analysis of sway tests, accuracy to linear hydrodynamic coefficients depends on the frequency of sway motion. To obtain nonlinear hydrodynamic coefficients for large drift angles, a combined yaw test is introduced. Using this system identification method, runs of PMM test can be reduced while retaining sufficient accuracy, compared to the Fourier integration method. Through the comparisons with sea trial results and the Fourier integration method, the accuracy and efficiency of the newly proposed system identification method, based on least square method, has been validated.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.288-295
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• 2022

This study shows a system identification method of a balancing loading device for a stair climbing delivery robot. The balancing loading device is designed as a 2-axes gimbal structure and is interpreted as two independent pendulum structures for simplifying. The loading device's properties such as mass, moment of inertia, and position of the center of gravity are changeable for luggage. The system identification process of the loading device is required, and the controller should be optimized for the system in real-time. In this study, the system identification method is based on least squares method to estimate the unknown parameters of the loading device's dynamic equation. It estimates the unknown parameters by calculating them that minimize the error function between the real system's motion and the estimated system's motion. This study improves the accuracy of parameter estimation using a null space solution. The null space solution can produce the correct parameters by adjusting the parameter's relative sizes. The proposed system identification method is verified by the simulation to determine how close the estimated unknown parameters are to the real parameters.

• Journal of Industrial Technology
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• v.19
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• pp.127-133
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• 1999

This paper is a research about system identification which optimizes uncertain geothechnical properties from the data measured during geotechnical design and construction. Various numerical optimization algorithms of Simplex method, Powell method, Rosenbrock method and Levenberg-Marquardt method were applied to the excavation problem to determine which method showed the best results with respect to robustness of success in finding an optimal solution to within a certain accuracy and number of function evaluations. From the results of numerical analysis, all of four algorithms are converged to exact solution after satisfying the allowed criteria, and Levenberg-Marquardt's algorithms was identified to be the most efficient method in number of function evaluations. System identification was applied to geotechnical engineering problems, possibly being occurred in field, to verify its applicability : estimation of settlement due to self-weight consolidation in dredged and filled soil. For self-weight consolidational settlement of a dredged soil, a program of evaluating the constitutive relationship of effective stress-void ratio-permeability was developed by using the technique of system identification. Thus, consolidational characteristics of a dredged soil, having a very high initial void ratio, can be evaluated.

• Journal of Power Electronics
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• v.17 no.6
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• pp.1545-1552
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• 2017

Identifying the load and mutual inductance is essential for improving the power transfer capability and power transfer efficiency of Inductive Power Transfer (IPT) systems. In this paper, a steady-state load and mutual inductance identification method focusing on series-parallel compensated IPT systems is proposed. The identification model is established according to the steady-state characteristics of the system. Furthermore, two sets of identification results are obtained, and then they are analyzed in detail to eliminate the untrue one. In addition, the identification method can be achieved without extra circuits so that it does not increase the complexity of the system or the control difficulty. Finally, the feasibility of the proposed method has been verified by simulation and experimental results.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.280-284
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• 1998

Many classes of nonlinear systems can be represented by Volterra kernel expansion. Therefore, identification of Volterra kernels of nonlinear system is an important task for obtaining the nonlinear characteristics of the nonlinear system. Although one of the authors has recently proposed a new method for obtaining the Volterra kernels of a nonlinear system by use of M-sequence and correlation technique, our mettled of nonlinear system identification is limited to Wiener-type nonlinear system and we can not apply this method to the identification of Hammerstein-type nonlinear system. This paper describes a new mettled for obtaining Volterra kernels of Hammerstein nonlinear system by adding a linear element in front of tile Hammerstein system. First we calculate the linear element of Hammerstein system by use of conventional correlation method. Secondly, we put a linear element in front of Hammerstein system. Then the total system becomes Wiener-type nonlinear system. Therefore we can use our method on Volterra kernel identification by use of M-sequence. Thus we get the coefficients of the approximation polynomial of nonlinear element of Hammerstein system. From the results of simulation, a good agreement with theoretical considerations is obtained, showing a wide applicability of our method.

• 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.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2143-2145
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• 2003

In the linear system identification using the discrete time constant coefficients, there is a subspace method based on 4SID recently much suggested instead of the parametric method like as the maximum likelihood method. The subspace method is not related with the impulse response and difference equation in its input-output equation, but with the system matrix of the direct state space model from the input-output data. The subspace method is a very useful tool to adopt in the multivariable system identification, but it has a shortage unable to adopt in the closed-loop system identification. In this paper, we are suggested the methods to get rid of the shortage of the subspace method in the closed-loop system identification. The subspace method is used in the estimate of the output prediction values from the estimating of the state space vector. And we have compared the results with the outputs of the recursive least square method in the numerical simulation.