• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11a
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• pp.409-412
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• 2001

Identification of systems operating in closed loop has long been of prime interest in industrial applications. The fundamental problem with closed-loop data is the correlation between the unmeasurable noise and the input. This is the reason why several methods that work in open loop fail when applied to closed-loop data. The prediction error based approaches to the closed-loop system are divided to direct method and indirect method. Both of direct and indirect methods are known to be applied to the closed-loop data without critical modification. But the direct method induces the bias error in the experimental frequency response function and this bias error may deteriorates the parameter estimation performance

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.78-86
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• 1999

The improvement of the water level control is important since it will prevent the steam generator trip so that improve the reliability and credibility of operation system. In this paper, the closed loop system identification is performed which can be used for the system monitoring and prediction of the system response. The model also can be used for the prediction control. Irving model is used as a steam generator model. The plant is an open loop unstable and non-minimum phase system. Fuzzy controller stabilize the system and the stable controller stabilize the system and the stable closed loop system is identified using neural networks. The obtained neural network model is validated using the untrained input and output. The results of computer simulation show the obtained Neural Network model represents the closed loop system well.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.644-646
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• 2004

The closed loop identification(CLID) is a very useful method for on-line applications since it can identify the plant in the closed-loop system composed of the plant and the controller. There are some literatures on CLID, but they and mainly focused on SISO(Single-Input/Single-Output) problem. In this paper, a CLID method is proposed for MIMO(Multi-Input/Multi-Output) systems. The CLID method is applied to a MIMO benchmark plant, TRMS(Twin-Rotor MIMO System). To illustrate the performance of the closed-loop system identification., unit step responses in the TRMS are represented and compared with the open-loop identification via some simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.140-145
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• 1994

In this paper, we consider a problem to estimate process parameters using input-output data collected from the process operating in closed-loop control system. When orders and delay-time of the process are known correctly, under some conditions of identifying experiments, it is reported that accurate identification results can be obtained by applying prediction error method. To get accurate estimates, it is necessary to know orders and delay-time of the process. It is difficult to determine them in closed-loop identification, because ill-condition for identification are easily caused by selection of unsuitable order or delay time. Furthermore, the procedures to select orders and delay-time in open-loop identification aren't always available in closed-loop identification. The purpose of this paper is to determine a delay-time under suitable assumption that order of the process are known as the first step.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.480-483
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• 1995

Identification of a process in closed-loop control system is an important problem in practice. This paper deals with parameter estimation using input-output data of the process operating in a closed-loop system. It is necessary to determine orders and delay-time to get consistent estimators by least square method for input-output data collected from the process. The authors considered a problem to determine delay-time in the condition that orders were known, in last KACC. So we extend the range to determine orders and delay-time in this paper.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.290-292
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• 1993

This paper investigates the identification of discrete time nonlinear system using neural networks with two hidden layers. A New learning method of both NNI and NNC is proposed. For control of the dynamical system we use two neural networks, one for identification and the other for control, and proposed NN control system is based on a framework of MRC. We define a closed loop error. In the proposed learning method, the identification error and the closed loop error are utilized to train the NNI, whareas the control error and the closed loop error are used to train the NNC, The simulation results show that the identification and control schemes suggested are practically feasible and effective.

• 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.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1632-1633
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• 2007

This paper presents a new method of designing digital controller based on closed-loop identification of a pulse width modulation (PWM) converter system. We consider the control system structure which is composed of both current control loop and voltage control loop. The current controller can be designed independently of voltage loop. Whereas voltage controller can not do easily due to the PWM switching component which is nonlinear in nature. Furthermore, the control objective of inner loop is to track the sine wave of 60 Hz, but the outer loop shall maintain the constant DC voltage irrespective to load change. To systematically design outer loop controller, we propose a method finding linear approximate model of the nonlinear inner loop part including current controller by closed loop identification. Based on the identified model, we show that a simple digital voltage controller can be directly designed and it has good performance.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2000.11a
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• pp.199-204
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• 2000

Generally, The systems are so complex that it not possible to obtain reasonable model using physical insight. Also a model based on physical insight contains a number of unknown parameters even if the structure is derived from physical laws. To solve these problems, the systems identification is described in this paper. So, AC servo motor system which has both open loop and closed loop is selected as an example for identification. A state-space model of AC servo motor system is identified through open loop experiment and identified through closed loop experiment and using pole placement integral controller to open loop system. As the results, From ARMA model, We have obtained continuous-time state space model.

• 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.