• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.5
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• pp.361-368
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• 2015

This paper deals with a robust fine seek controller design problem with multiple constraints using a genetic algorithm. A robust $H\infty$ constraint is introduced to attenuate effectively velocity disturbance caused by the eccentric rotation of the disk. A weighting function is optimally selected based on the estimation of velocity disturbance and the estimated minimum velocity loop gain. A robust velocity loop constraint is considered to minimize the variances of the velocity loop gain and bandwidth against the uncertainties of fine actuator. Finally, a robust fine seek controller is obtained by solving a genetic algorithm with an LMI condition and an appropriate objective function. The proposed controller design method is applied to the fine seek control system of a DVD recording device and is evaluated through the experimental results.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.26-26
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• 2000

We study on the velocity matching algorithm for transfer alignment of inertial navigation system(INS) using robust H$_2$ filter. We suggest an uncertainty model for INS and apply the suggested discrete robust H$_2$ filter to the uncertainty model compared with kalman filter, the discrete robust H$_2$ filter is shown by simulation to have good performance of alignment time and accuracy.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.8
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• pp.140-146
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• 1995

In case the uncertainty existing in a system is assumed to satisfy the matching condition and to be come-bounded. Y. H. Chen proposed an adaptive robust control algorithm which introduced adaptive sheme for a design parameter into robust deterministic controls. In this paper, the adaptive robust control algorithm is applied to the position tracking control of direct drive robots, and simulation and experimental studies are conducted to evaluate control performance.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.293-298
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• 1997

The H$_{\infty}$robust controller fur the reactor power control system is designed by use of the mixed weight sensitivity. The system is configured into the typical two-port model with which the weight functions are augmented. Since the solution depends on the weighting functions and the problem is of non-convex, the genetic algorithm is used to determine the weighting functions. The cost function applied in the genetic algorithm permits the direct control of the power tracking performances. In addition, the actual operating constraints such as rod velocity and acceleration can be treated as design parameters. Compared with the conventional approach, the controller designed by the genetic algorithm results in the better performances with the realistic constraints. Also, it is found that the genetic algorithm could be used as an effective tool in the robust design. robust design.

• Journal of information and communication convergence engineering
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• v.8 no.1
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• pp.23-28
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• 2010

According to increasing projection order, the AP algorithm bas noise amplification problem in large background noise. This phenomenon degrades the performances of the AP algorithm. In this paper, we analyze convergence characteristic of the AP algorithm and then suggest a noise robust modified AP algorithm for reducing this problem. The proposed algorithm normalizes the update equation to reduce noise amplification of AP algorithm, by adding the multiplication of error power and projection order to auto-covariance matrix of input signal. By computer simulation, we show the improved performance than conventional AP algorithm.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.52 no.2
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• pp.74-80
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• 2003

In this paper, a robust two-degree-of-freedom(TDF) the speed control system using $H_{\infty}$ optimization method and real genetic algorithm is proposed for the robust stability and the robust performance in dc servo motor system. This control system composed of feedback and feedforward controller. The feedback(FB) controller with $H_{\infty}$ optimization method is designed for real genetic algorithm that is model matching problem using mixed sensitivity function. The feedforward(FF) controller with $H_{\infty}$optimization method is minimized the error between transfer function of the optimal model and the overall transfer function. The proposed robust two-degree-of-freedom speed control system is simulated to the dc servo motor. By the simulation, feedback controller can obtain the robust stability property and feedforward controller can obtain the robust performance property under modelling error. The performance of the dc servo motor is analyzed by the experiment setting. The validity of the proposed method is verified through being compared with pid(proportional integrated differential)control system design method for the dc servo motor.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.642-647
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• 1993

In general, systems contain uncertain elements in the real world; these may be parameters, constant or varying, that are unknown or imperfectly known. When the uncertainty is assumed to satisfy the matching condition and to be cone-bounded, Y.H. Chen[81 proposed an adaptive robust control algorithm which introduced adaptive scheme for a design parameter into robust deterministic controls. In this paper, the above control algorithm is applied to the position tracking control of 2 DOF direct drive SCARA robots, and simulation and experimental studies are conducted to verify the control algorithm and to evaluate control performance.

• The Journal of the Acoustical Society of Korea
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• v.17 no.1E
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• pp.10-13
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• 1998

We propose a robust non-speech rejection algorithm using the three types of pitch-related parameters. The robust non-speech rejection algorithm utilizes three kinds of pitch parameters : (1) pitch range, (2) difference of the successive pitch range, and (3) the number of successive pitches satisfying constraints related with the previous two parameters. The acceptance rate of the speech commands was 95% for -2.8dB signal-to-noise ratio (SNR) speech database that consisted of 2440 utterances. The rejection rate of the non-speech sounds was 100% while the acceptance rate of the speech commands was 97% in an office environment.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2262-2267
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• 2018

In electric machine design, there is a large computation cost for finite element analyses (FEA) when analyzing nonlinear characteristics in the machine Therefore, for the optimal design of an electric machine, designers commonly use an optimization algorithm capable of excellent convergence performance. However, robustness consideration, as this factor can guarantee machine performances capabilities within design uncertainties such as the manufacturing tolerance or external perturbations, is essential during the machine design process. Moreover, additional FEA is required to search robust optimum. To address this issue, this paper proposes a computationally efficient robust optimization algorithm. To reduce the computational burden of the FEA, the proposed algorithm employs a useful technique which termed static analysis assisted technique (SAAT). The proposed method is verified via the effective robust optimal design of electric machine to reduce cogging torque at a reasonable computational cost.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.438-441
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• 2001

This paper describes a system for tracking a face in a input video sequence using facial convex hull based facial segmentation and a robust hausdorff distance. The algorithm adapts YCbCr color model for classifying face region by [l]. Then, we obtain an initial face model with preprocessing and convex hull. For tracking, a Robust Hausdorff distance is computed and the best possible displacement is selected. Finally, the previous face model is updated using the displacement t. It is robust to some noises and outliers. We provide an example to illustrate the proposed tracking algorithm in video sequences obtained from CCD camera.