• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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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.

• Journal of Mechanical Science and Technology
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• v.16 no.3
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• pp.327-337
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• 2002

This paper applies a recently developed on-line parameter identification (PID) technique to sets of real flight data and compares the results with those of a state-of-the-art off-line PID technique. The on-line PID technique takes Linear Regression from Fourier Transformed equations and the off-line PID is based on the traditional Maximum Likelihood method. Sets of flight data from the NASA F/A-18 High Alpha research Vehicle (HARV) circraft, which has been recorded from specifically designed maneuvers and used for our line parameter estimation, are used for this study. The emphasis is given on the accuracy and on-line measure of reliability of the estimates. The comparison is performed for both longitudinal and lateral-directional dynamics for maneuvers at angles of attack ranging u=20°through $\alpha$=40°. Results of the two estimation processes are also compared with baseline wind tunnel estimates whenever possible.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2929-2931
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• 1999

In this paper, an adaptive fuzzy inference and HCM(Hard C-Means) clustering method are used for on-line fuzzy modeling of nonlinear and complex system. Here HCM clustering method is utilized for determining the initial parameter of membership function of fuzzy premise rules and also avoiding overflow phenomenon during the identification of consequence parameters. To obtain the on-line model structure of fuzzy systems. we use the recursive least square method for the consequent parameter identification. And the proposed on-line identification algorithm is carried out and is evaluated for sewage treatment process system.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1001-1002
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• 2007

This study investigates the dynamic characteristics of Synchronous Reluctance Motor (SynRM), with segmental rotor structure, using finite element method in which the moving mesh technique is considered. The focus of this paper is the efficiency of on-line parameter identification system for position sensorless control of a SynRM under saturation and iron loss. Comparisons are given with angle of the observer and those of proposed FEM & Preisach model of synchronous reluctance motor, respectively. The position sensorless control using identified motor parameters is realized, and the effective of the on-line parameter identification system is verified by experimental results.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.4B no.4
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• pp.206-210
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• 2004

This paper deals with a parameter estimation of the DC-DC converter system for its diagnosis. Especially, we present the results of parameter estimation for the DC-DC converter model by the system identification method. The parameter estimation for the DC-DC converter system aims at the diagnosis of its operating status. For the operating status diagnosis of the DC-DC converter system, we assume that the DC-DC converter system is an equivalent model of the Buck converter and estimate the main parameter for on-line diagnosis. In addition, for verification of an estimated parameter, we compare a bode plot of the estimated system transfer function and measurement results of the HP4194 instrument. It is a control system analyzer for system transfer function measurement. Our results confirm that the main parameter for diagnosis of the DC-DC converter system can be estimated by the system identification method and that the aging status of the system can be predicted by these results on operating status.

• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.2
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• pp.3-9
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• 2012

The paper describes a novel technique for on line parameter identification of three-phase induction motors from a single, run up to speed test. Data is sampled during this test with the normal locked rotor and synchronous speed data captured on the way to reaching the motor's rated speed. Rotor parameter variations with frequency due to skin and proximity effects and other non-linear imperfections such as heating and main flux path saturation are taken into account. This method is ideal for determining and/or verifying parameters used in high performance drives.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.12
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• pp.2103-2108
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• 2007

This study investigates the dynamic characteristics of Synchronous Reluctance Motor (SynRM), with segmental rotor structure, using finite element method in which the moving mesh technique is considered. The focus of this paper is the efficiency of on-line parameter identification system for position sensorless control of a SynRM considering saturation and iron loss. Comparisons are given with angle of the observer and those of proposed FEM & Preisach model of synchronous reluctance motor, respectively. The position sensorless control using identified motor parameters is realized, and the efficiency of the on-line parameter identification system is verified by experimental results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.3
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• pp.376-382
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• 2014

E-Actuator is an essential part of an eVGT, it receives the command from the main ECU and controls the vane. An e-Actuator failure can cause an abrupt change in engine output and it may induce an accident. Therefore, it is required to detect anomalies in the e-Actuator in real time to prevent accidents. In this paper, an e-Actuator fault detection method using on-line parameter identification is proposed. To implement on-line fault detection algorithm, many constraints are considered. The test input and sampling rate are selected considering the constraints. And new recursive system identification algorithm is proposed which reduces the memory and MCU power dramatically. The relationship between the identified parameters and real elements such as gears, spring and motor are derived. The fault detection method using the relationship is proposed. The experiments with the real broken gears show the effectiveness of the proposed algorithm. It is expected that the real time fault detection is possible and it can improve the safety of eVGT system.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.710-713
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• 1995

Hopfield networks have been applied to the problem of linear system identification. In this paper, Hopfield network based parameter identification scheme of non-linear dynamic systems is proposed. Simulation results demonstrate that Hopfield network can be used effectively for the identification of non-linear systems assuming that the system states and their time derivatives are available. Therefore, the proposed scheme can be applied in fault detection and isolation(FDI) and adaptive control of non-linear systems where the Hopfield networks perform on-line identification of system parameters.

• 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.