• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.4
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• pp.76-83
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• 2001

In this study, the static and dynamic characteristics of turning process was modelled and the analytic realization of regen-erative chatter mechanism was discussed. In this regard, we have discussed on the comparative assessment of recursive times series modeling algorithms that can represent the machining process and detect the abnormal machining behaviors in precision turning operation. In this study, simulation and experimental work were performed to show the malfunction behaviors. For this purpose, new Recursive Extended Instrument Variable Method(REIVM) was adopted for the on-line system identification and monitoring of a machining process. Also, we can apply REIVE algorithms in real process for the detection of chatter frequency and dynamic property and analyze the stability lobe of the system by changing a parameter of cutting dynamics in regenerative chatter mechanics, if it is stable or unstable, Also, The stability lobe of chatter was analysed.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.789-797
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• 2017

Recently, some integrated structural identification/damage detection and reliability evaluation of structures with uncertainties have been proposed. However, these techniques are applicable for off-line synthesis of structural identification and reliability evaluation. In this paper, based on the recursive formulation of the extended Kalman filter, an on-line integration of structural identification/damage detection and reliability evaluation of stochastic building structures is investigated. Structural limit state is expanded by the Taylor series in terms of uncertain variables to obtain the probability density function (PDF). Both structural component reliability with only one limit state function and system reliability with multi-limit state functions are studied. Then, it is extended to adopt the recent extended Kalman filter with unknown input (EKF-UI) proposed by the authors for on-line integration of structural identification/damage detection and structural reliability evaluation of stochastic building structures subject to unknown excitations. Numerical examples are used to demonstrate the proposed method. The evaluated results of structural component reliability and structural system reliability are compared with those by the Monte Carlo simulation to validate the performances of the proposed method.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.578-580
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• 1998

The paper presents an approach to the structure identification based on the CART (Classification And Regression Tree) algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy system. By using the CART algorithm, the proposed method can roughly estimate the numbers of membership function and fuzzy rule using the centers of decision regions. Then the parameter identification is carried out by the hybrid learning scheme using BP (Back-propagation) and RLSE (Recursive Least Square Estimation) from the numerical data. Finally, we will show it's usefulness for fuzzy modeling to truck backer upper control.

• 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.

• Transactions of the Korean Society of Mechanical Engineers
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• v.12 no.1
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• pp.163-172
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• 1988

An off-line Geometric Adaptive Control Scheme is applied to the milling machine to identify its guideway errors. In the milling process, the workpiece fixed on the bed travels along the guideway while the tool and spindle system is fixed onto the machine. The scheme is based on the exponential smoothing of post-process measurements of relative machining errors due to the tool, workpiece and bed deflections. The guideway error identification system consists of a gap sensor, a, not necessarily accurate, straightedge, and the numerical control unit. Without a priori knowledge of the variations of the cutting parameters, the time-varying parameters are also estimated by an exponentially weighted recursive least squares method. Experimental results show that the guideway error is well identified within the range of RMS values of geometric error changes between machining passes disregarding the machining conditions.

• Journal of Electrical Engineering and Technology
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• v.7 no.1
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• pp.132-140
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• 2012

A non-conservative robust Kalman filter (NCRKF) is applied to flight data to identify the aerodynamic derivatives of an unmanned autonomous vehicle (UAV). The NCRKF is formulated using UAV lateral motion data and then compared with results from the conventional Kalman filter (KF) and the recursive least square (RLS) method. A superior performance for the NCRKF is demonstrated by simulation and real flight data. The NCRKF is especially effective in large uncertainties in vehicle modeling and in measuring flight data. Thus, it is expected to be useful in missile and aircraft parameter identification.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.148-151
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• 1993

Load models for the analysis and simulation of power system are often introduced when the more accurate result is required. This work presents a single expressed load model as T-equivalent circuit of induction motor, for the composite characteristics of various loads. The parameters of the proposed load model are identified based on the system identification method as Recursive Least Square identification method. Case study results show the accuracy of proposed load model, and compared with some field measurements.

• 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.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.204-210
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• 2005

This paper presents online identification algorithm for TSK fuzzy model. The proposed algorithm identify structure of premise part by using distance, and obtain the parameters of the piecewise linear function consisting consequent part by using recursive least square. Only input space was considered in Most researches on structure identification, but input and output space is considered in the proposed algorithm. By doing so, outliers are excluded in clustering effectively. The existing other algorithm has disadvantage that it is sensitive to noise by using data itself as cluster centers. The proposed algorithm is non-sensitive to noise not by using data itself as cluster centers. Model can be obtained through one pass and it is not needed to memorize many data in the proposed algorithm.