• 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.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.206-211
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• 1993

This paper presents a new identification method which utilizes fuzzy inference in parameter identification. The proposed system has an additional control loop where a real plant is replaced by a plant model. The control system to be designed is to satisfy the following specifications: 1) It has zero steady-state error. 2) It has adequate damping characteristics. 3) 1),2) satisfied, it has a shortest rise-time. Fuzzy rules describe the relationship between comparison results of the features and magnitude of modification in the model parameter values. This method is effective in auto-tuning because the response of the closed loop is verified. The proposed method is tested in simulation for several plants with high-order lags and dead-times.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.214-217
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• 1993

This paper presents a new identification method which utilizes fuzzy inference in parameter identification. The proposed system has an additional control loop where a real plant is replaced by a plant model. The control system to be designed is to satisfy the following specifications: 1) It has zero steady-state error. 2) It has adequate damping characteristics. 3) 1),2) satisfied, it has a shortest rise-time. Fuzzy rules describe the relationship between comparison results of the features and magnitude of modification in the model parameter values. This method is effective in auto-tuning because the response of the closed loop is verified. The proposed method is tested in simulation for several plants with first- order lags and dead-times. The results show that the proposed method is effective in practical use.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.1334-1337
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• 2006

System identification is the field of modeling dynamic systems from experimental data. As a modeling technique, we can mention finite element method (FEM). In addition, we are able to measure modal data as the experimental data. The system can be generally categorized into a gray box and black box. In the gray box, we know mathematical model of a system, but we don't know structural parameters exactly, so we need to estimate structural parameters. In the black box, we don't know a system completely, so we need to identify system from nothing. To date, various system identification methods have been developed. Among them, we introduce system realization theory which uses Hankel matrix and Eigensystem Realization Algorithm (ERA) that enable us to identify modal parameters from noisy measurement data. Although we obtain noise-free data, however, we are likely to face difficulties in identifying a structure with hidden modes. Hidden modes can be occurred when the input or output position comes to a nodal point. If we change a system using a mode decoupling controller, the hidden modes can be revealed. Because we know the perturbation quantities in a closed loop system with the controller, we can realize an original system by subtracting perturbation quantities from the closed loop system. In this paper, we propose a novel method to identify a structure with hidden modes using the mode decoupling controller and the associated example is given for illustration.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.248-254
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• 2018

In this paper, a Monte Carlo-based Recursive Least Square(MC-RLS) method is presented to directly identify the inverse model of the dynamical system. Although a RLS method has been used for the identification based on the deterministic data in the closed loop controlled form, it would be better for RLS to identify the model with random data. In addition, the inverse model obtained by inverting the identified forward model may not work properly. Therefore, MC-RLS can be used for the inverse model identification without proceeding a numerical inversion of an identified forward model. The performance of the proposed method is verified through experimental studies on a control moment gyroscope.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.556-559
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• 1999

In this paper, the Observer Controller IDentification/Eigensystem Realization Algorithm(OCID/ERA) is applied to identify the state space model of a helicopter in hover from a given set of the general input-output data. The objective of this paper is the evaluation of the utility of the OCID/ERA approach to the system identification of a system which has natural unstable mode like a helicopter and to give some guidelines before applying the technique to the real system. As it mentioned above, since a model helicopter is unstable naturally, the closed-loop system identification method(OCID/ERA) using excitation, output and feedback signals of the simulated system is applied.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.153-156
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• 1999

The technique is reported of identifying parameters in off-line process. The technique demands that closed-loop system consists of a reference and two-degree-of-freedom controllers (TDFC) in real process. A model process is the same as the real process except their parameters. Deviations are differences between the reference and the output of the plant or the model. The technique is based on minimizing identification error between the two deviations. The parameter differences between the plant and the model are characterized of mean value and of variance which are derived from the identification error. Consequently, the algorithm which identifies the unknown plant parameters is shown by minimizing the mean value and the variance, respectively, within double convergence loops. The technique is applied to course change of a ship. The plant deviation at the first trial is shown to occur in replacing the nominal parameters by the default parameters. The plant deviation at the second trial is shown to not occur in replacing the nominal parameters by the identified parameters. Hence, the identification technique is confirmed to be feasible in the real field.

• Journal of Power System Engineering
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• v.5 no.4
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• pp.44-52
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• 2001

This paper considers a modeling and identification for the MIMO magnetic bearing system. To obtain the nominal plant transfer functions, we have experimented on the frequency response by a closed-loop identification method because the system is unstable essentially. We suggest a method of curve-fitting for obtaining the transfer function from the frequency responses by using the system's modeling structure and two controllers which are different from each other. From the frequency response results, we found the effects of coupling by opposing controllers. And using this effects and the system's modeling structure, we could obtain the transfer functions of which have the same modularized denominators.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.304-306
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• 1993

In this paper, the authors show a link between a heuristic controller used in industry and a theoretical generalized controller. First, we clarify the internal structure of the generalized two-degree-of-freedom controller which yields a link between the theoretical researches and the practical applications. Secondly, we indicate how to blend identification and control together without any modification of the controller. This is in fact the problem of closed-loop identification. Thirdly, we propose a design technique of a free parameter taking into account a robust stability based on the information obtained from the identification. Finally, we apply the proposed algorithm to trajectory control of DD robot.