• 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.

• Journal of Industrial Technology
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• v.28 no.B
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• pp.167-174
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• 2008

In this paper, the analysis on type effect of inverter output voltage distortion on the control of an induction motor is discussed. The inverter output voltage is distorted differently from the reference voltage owing to the inverter nonlinear characteristic. The inverter nonlinear characteristic results from the voltage drop, the inherent characteristic of the power semiconductor, and the dead time for preventing the short circuit of the inverter leg. This characteristic distorts the inverter output voltage and then, causes the motor flux estimation error. Although this characteristics do not significantly effect in the general-purpose induction motor control, but significantly effect on the low-speed operation of high performance motor control such as the sensorless vector control.

• Journal of Power Electronics
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• v.10 no.3
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• pp.270-276
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• 2010

In this paper, a capacitance estimation scheme for DC-link capacitors for single-phase AC/DC PWM converters is proposed. Under the no-load condition, a controlled AC current (30[Hz]) is injected into the input side, which then causes AC voltage ripples at the DC output side. Or, a controlled AC voltage can be directly injected into the DC output side. By extracting the AC voltage/current and power components on the DC output side using digital filters, the capacitance value can be calculated, where the recursive least squares (RLS) algorithm is used. The proposed methods can be simply implemented with software only and additional hardware is not required. From the experiment results, a high accuracy estimation of capacitances less than 0.85% has been obtained.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.110-110
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• 2001

By the state transformation including independent outputs functions, a nonlinear process model can be decomposed into two subsystems; the one(design model) is described in output variables as new states and used for control system synthesis and the other(disturbance model) is described in the original unavailable states and its couplings with the design model are treated as uncertain time-varying parameters in the design model. Its existence with respect to the design model is ignored. So, the design model is and uncertain time-variant system. Control synthesis based on a reduced design model is a combined form of a time-variant input-output linearization with parameter estimation. The parameter estimation is also based on the design model and it gives the parameter estimates such that the estimated outputs follow the actual outputs in a specified way. The disturbances form disturbance model and as well all the other uncertainties affecting the outputs will be reflected into the estimated parameters used in the linearizing control law.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.54-57
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• 2002

In this paper model reference adaptive control (MRAC) of linear time-varying(LTV) systems is considered. MRAC for a linear time invariant(LTI) system does not assure the boundedness of the output and parameter estimation errors in the presence of time variations of the parameters. However, changing the adaptive laws such as use of $\sigma$-modification can result in the boundedness of the output and parameter estimation errors[5]. Together with the $\sigma$-modification in the adaptive law, we also modify the control law by adding an additional term to the standard control law. The additional term leads to smaller bounds of the output and parameter estimation errors when compared to the case where only the standard control law is applied.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.2
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• pp.137-143
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• 1989

System modeling from input-output data is generally carried out in two steps. The first step is to determine the form of the model. In the second step, the parameters of the model in an appropriate form are estimated from input-output data. This paper presents a method, via single term Walsh functions, for simultaneous estimation of the order and the parameters of linear systems from input-output data. The estimation of the model order is based on minimizing an error function, which is defined by Desai and Fairman. Unknown system parameters are recursively estimated by the least square method.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.10
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• pp.727-732
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• 1988

In this paper, a parameter estimation method of linear systems with bounded output disturbances is studied. The bound of the disturbances is assumed to known Weighting factors are proposed to modify LS(Least Square) algorithm in the parameter estimation method. The conditions of weighting factors are given so that the estimation method has good convergence properties. This condition is more relaxed form than other known conditions. The compensation term in the estimation equations is represented by a function of the output prediction error and this function should lie in a specified region on x-y plane to satisfy these conditions of weighting factors. A set of weighting factor is selected and an algorithm is proposed using this set of weighting factor. The proposed algorithm is compared with another existing algorithm by simulation and its performance in parameter estimation id discussed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.713-717
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• 2014

This paper presents a beamforming technique for locating impulsive sound source. The conventional frequency-domain beamformer is advantageous for localizing noise sources for a certain frequency band of concern, but the existence of many frequency components in the wide-band spectrum of impulsive noise makes the beamforming image less clear. In contrast to a frequency-domain beamformer, it has been reported that a time-domain beamformer can be better suited for transient signals. Although both frequency- and time-domain beamformers produce the same result for the beamforming power, which is defined as the RMS value of its output, we can use alternative directional estimators such as the peak value and crest factor to enhance the performance of a time-domain beamformer. In this study, the performance of three different directional estimators, the peak, crest factor and RMS output values, are investigated and compared with the incoherent interfering noise embedded in multiple microphone signals. The proposed formula is verified via experiments in an anechoic chamber using a uniformly spaced linear array. The results show that the peak estimation of beamformer output determines the location with better spatial resolution and a lower side lobe level than crest factor and RMS estimation in noise free condition, but it is possible to accurately estimate the direction of the impulsive sound source using crest factor estimation in noisy environment with stationary interfering noise.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.232-234
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• 1996

Cadiac output estimation scheme of LVAD using multi-dimensional interpolation technique was introduced in this paper. This paper also show appropriate input -output data for estimation. Experimental results show our approach is a good one for the estimation of nonlinear hemodynamics.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.101.5-101
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• 2001

A linear input/output data-based predictive control with integral action is developed. The control input is obtained directly from the input/output data in a single step. However, the state estimation in subspace identification gives a biased estimate and there is model mismatch when the controller is applied to a nonlinear process. To overcome such difficulties, we add integral action to a linear input/output data-based predictive controller by augmenting the integrated white noise disturbance model and use each of best linear unbiased estimation(BLUE) filter and Kalman filter as a stochastic observer for the unmeasured disturbance. When applied to a continuous styrene polymerization reactor the proposed controller demonstrates.