• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1477-1480
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• 1997

This paper is concerned with the design of a suboptimal robust Kalman filter using LMI approach for system models in the state space, which are subjected to parameter uncertainties in both the state and measurement atrices. Under the assumption that augmented system composed of the uncertain system and the state estimation error dynamics should be stable, a Lyapunov inequality is obtained. And from this inequaltiy, the filter design problem can be transformed to the gneric LMI problems i.e., linear objective minimization problem and generalized eigenvalue minimization problem. When applied to uncertain linear system modles, the proposed filter can provide the minimum upper bound of the estimation error variance for all admissible parameter uncertainties.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.413-418
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• 2003

In this paper, a new multirate optimal finite impulse response (FIR) filter is proposed for the signal reconstruction in the nosy filter bank systems. The multirate optimal FIR filter replaces the conventional synthesis filters and the Kalman synthesis filter. First, the generic linear model is derived from the multirate state space model for an autoregressive (AR)input signal. Second, the multirate optimal FIR filter is derived from the multirate generic linear model using the minimum variance criterion. This paper also provides numerical examples and results. The simulation results illustrate that the performance is improved compared with conventional synthesis filters and the proposed filter has advantages over the Kalman synthesis filter.

• Journal of the Korea Convergence Society
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• v.4 no.4
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• pp.41-48
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• 2013

Observer design for system with unknown input was carried out. First, Kalman filter was considered to estimate system state with White noise. With the results of Kalman filter design, state observer, controller properties, including controllability and observability, and the Kalman filter structure and algorithm were also studied. Kalman filter algorithm was applied to Position and velocity measurement based on Kalman filter with white noise, and it was constructed and achieved by programming based on Matlab programming. Finally, observer for system with unknown input was constructed with the help of Gershgorin's disc theorem. With the designed observer, system states was constructed and applied to system with unknown input. By simulation results, estimation performance was verified. In this project, state feedback control theory, observer theory and relevant design procedure, as well as Kalman filter design were understood and used in practical application.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.527-530
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• 1996

The Kalman filter has been used as a self-localization method for the mobile robot. To satisfy the assumptions inherent in the Kalman filter, we should calibrate the sensors of the robot before use of them. However, it is generally hard to find exact sensor parameters, and the parameters may change during the robot task as the environment varies. Thus we need to perform on-line sensor calibration, by which we can obtain more credible location of the mobile robot. In this paper, we present an on-line sensor calibration scheme which estimates the unknown sensor bias and the current position of the robot. To this end, first we find out the calibration errors of the sensor from redundant sensory data using the parity vector and recursive minimum variance estimation. Then we calculate the current position of the robot by weighted least square estimation without internal encoder data. The performance of the proposed method is evaluated through computer simulation.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.135-137
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• 1992

A new algorithm for self-tuning digital controller is proposed. The system to be controlled is identified on line in auto-regressive-moving-average(ARMA) form via recursive least mean square method. The control law is obtained from the minimization of an objective function. The proposed objective function is similar to that of Generalized Minimum Variance(GMV) method but modified to lessen the overshoot and to avoid numerical divergence problem. This algorithm is applied to the power system stabilization and the comparison of the proposed method with a conventional power system stabilizer(PSS) is presented.

• Journal of the Semiconductor & Display Technology
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• v.14 no.2
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• pp.47-52
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• 2015

In this paper, the performance of uncoated- and Titanium nitride aluminium TiAlN-PVD coated- carbide twist drills were investigated when drilling aluminium alloy, Al 6061. This research focuses on the optimization of drilling parameters using the Taguchi technique to obtain minimum surface roughness and thrust force. A number of drilling experiments were conducted using the L9 orthogonal array on a CNC vertical machining center. The experiments were performed on Al 6061 material l blocks using uncoated and coated HSS twist drills under dry cutting conditions. Analysis of variance(ANOVA) was employed to determine the most significant control factors. The main objective is to find the important factors and combination of factors influence the machining process to achieve low surface roughness and low cutting thrust force. From the analysis of the Taguchi method indicates that among the all-significant parameters, feed rate are more significant influence on surface roughness and cutting thrust than spindle speed.

• The Korean Journal of Applied Statistics
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• v.5 no.1
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• pp.19-28
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• 1992

In this paper, minimal balanced $2^t$ fractional factorial designs which permit the estimation of main effects and 2-factor interactions are developed by using a partially balanced array. Such designs are characterized by a minimum number of runs and some balancedness property of the variance-covariance matrix of the estimates. In addition to describing the designs, optimality criteria are discussed and the trace-optimal designs are presented. The proposed designs are especially useful in Taguchi method, where we need to investigate up to 2-factor interactions of the control factors.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.324-329
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• 2003

An air-conditioner has various noise sources such as cooling fan noise, pumping noise, flow noise and impact noise. Among these, impact noise is the most unpleasant source. This is because the noise is produced in indoor unit of air-conditioner. To control the noise source effectively, first we must identify the noise sources. When we identify impact noise source, the measurement have to be carried out simultaneously. So we use beamforming method that requires less measurement points than intensity method and acoustic holography. The objective of this paper is to estimate the location of impact source. This objective can be achieved by using minimum variance cepstrum that is able to detect impulse embedded in noise. In this study, modified beamforming method based on cepstrum domain is proposed. Then this method applied to air-conditioner noise sources which produce impact noise.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.592-594
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• 1998

In the single-input and single-output system, the parameter of plant is scalar polynomial, but in the multiple input and multiple output, it accompanies, being matrix polynomial, the consideration of observable conrolability index or problems of non-commutation in matrix polynomial as well as degree, and it is more complex to deal with. Therefore, it is thought that a full reserach on the single-input and single-output system is not made. This reserach propose that problems of minimum variance self-tuning regulator of multivariable system and pole placement self-tuning regulator.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.3
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• pp.399-406
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• 1996

This paper presents a method of hierarchical state estimation of the time-varying linear systems via Block-pulse function(BPF). When we estimate the state of the systems where noise is considered, it is very difficult to obtain the solutions because minimum error variance matrix having a form of matrix nonlinear differential equations is included in the filter gain calculation. Therefore, hierarchical approach is adapted to transpose matrix nonlinear differential equations to a sum of low order state space equation from and Block-pulse functions are used for solving each low order state space equation in the form of simple and recursive algebraic equation. We believe that presented methods are very attractive nd proper for state estimation of time-varying linear systems on account of its simplicity and computational convenience. (author). 13 refs., 10 figs.