• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.04a
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• pp.160-166
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• 1997

The spindle motor in a computer hard disk drive can be modeled as a rotor-bearing system supported by the base plate. Ball bearing is the crucial element to determine the stiffness of the spindle motor, and its design parameters and operating conditions determine the dynamic characteristics of the spindle motor. In the analysis of a rotor-bearing system with a short shaft like a spindle motor, the stiffness of the base plate as well as ball bearings must be considered accurately to analyze the dynamic charateristics of a spindle motor. In this paper, the lateral and the axial vibration of the spindle motor were analyzed by the transfer matrix method for the dual-shaft rotor-bearing model and by d.o.f lumped parameter model, respectively. The simulation results had good agreements with the experimental modal testing. The dynamic characteristics were fully investigated for the change of the major design parameters of the spindle motor, i.e. the preload of ball bearings and the rotational speed.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.232-236
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• 2000

In this paper a novel estimation algorithm of mechanical parameters in two-mass system is proposed. The inertia of a load and a motor and the stiffness are estimated by using RLS (Recursive Least Square) algorithm and acceleration information of motor. The effectiveness of the proposed scheme is verified with simulation and experiments results.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2004.11a
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• pp.16-16
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• 2004

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.8-15
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• 2019

The PM synchronous motor drives with vector control have been applied to wide fields of industry applications due to its high efficiency. The rotor position information for vector control of a PM synchronous motor is detected from the rotor position sensors or rotor position estimators. The sensorless control based on the mathematical model of PM synchronous motor is generally used and it can be classified into back EMF -based sensorless control and magnet flux-based sensorless control. The rotor position estimating performance of the back EMF-based sensorless control is deteriorated at low speeds since the magnitude of back EMF is proportional to the motor speed. The magnitude of the magnet flux for estimating rotor position in the flux-based sensorless control is independent on the motor speed so that the estimating performance is excellent for wide speed ranges. However, the estimation performance of the model-based sensorless control may be influenced by the motor parameter variation since the rotor position estimator uses the mathematical model of the PM synchronous motor. In this paper, the rotor position estimation performance for the back EMF based- and flux-based sensorless controls is analyzed theoretically and is compared through the simulation and experiment when the motor parameters including stator resistance and inductance are varied.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.195-198
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• 1997

This paper proposes the effective compensation method of the rotor time constant of induction motor. An indirect vector control method is highly dependent on the motor parameters. To solve the problem of performance degradation due to parameter variation in an indirect vector control of induction motor, we compensate the rotor time constant by current error feedback. The proposed method is a simple on-line rotor time constant compensation method using the information from terminal voltages and currents. As the current error, difference between current command and estimated current, approaches to zero, the value of rotor time constant in an indirect vector controller follows the real value of induction motor. This scheme is valid transient region as well as steady state region regardless of low or high speed. This method is verified by computer simulation. For this, we constructed the simulation model of induction motor, indirect vector controller and current regulated PWM (CRPWM) voltage source inverter (VSI) using SIMULINK in MATLAB.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.1
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• pp.24-30
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• 1996

BLDC motor is widely used as a servo motor, because it has high efficiency, high power ratio, low inertia, and easy maintenance. However, position and speed sensors generally attached in BLDC motor increase motor cost, and limit environments of application. This study describes a sensorless speed control of sinusoidal BLDC motor using the d-q transformed instantaneous voltage equation, and presents the result of computer simulation. The sensorless algorithm is applied to the casse of a voltage controlled PWM inverter. The result indicates good dynamics and a robust control in cases of a load change and a system parameter variation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.11
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• pp.1813-1819
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• 2016

In this paper, LS-SynRM (Line Start-Synchronous Reluctance Motor) has been attracting attention in replace of induction motor which hardly provides high efficiency. Compared to induction motor, LS-SynRM has better efficiency per unit area. This study demonstrated the electromagnetic design methods of LS-SynRM while maintaining the frame of existing IE3 induction motor for blower. We documented the design procedures for generating high saliency which is the most essential and mechanical stress analysis is also treated. In conclusion, we proved the validity of our design by manufacturing and testing our LS-SynRM models.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.207-213
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• 2000

This paper introduces a high-performance speed control system based on artificial neural networks(ANN) to estimate unknown parameters of a DC servo motor. The goal of this research is to keep the rotor speed of the DC servo motor to follow an arbitrary selected trajectory. In detail, the aim is to obtain accurate trajectory control of the speed, specially when the motor and load parameters are unknown. By using an artificial neural network, we can acquire unknown nonlinear dynamics of the motor and the load. A trained neural network identifier combined with a reference model can be used to achieve the trajectory control. The performance of the identification and the control algorithm are evaluated through the simulation and experiment of nonlinear dynamics of the motor and the load using a typical DC servo motor model.

• Proceedings of the KIEE Conference
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• 2004.04a
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• pp.6-8
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• 2004

Recently, it is increasing to use more Brushless DC Motor with high energy density permanent magnet and semiconductor control unit for complementing the mechanical defect of Brushed DC Motors. For designing of BLDC Motors are required complex parameters like as rated characteristic, Geometries, B-H curve of magnet and steel materials, winding factor, etc. Moreover, design and manufacturing are difficult because of additional control circuits. Generally, Design parameters are gotten by analysis of Motor which is used. And the design parameters are used to design a new motor. But getting the design parameters through the eyes and experience is limited and it takes a long time. In this paper a method is proposed to efficiently analyze motor design parameters through the No load and Load Test, Back EMF Test, Simulation Analysis and Patent Analysis Method for existing BLDC Motor for a cooling Fan in Vehicle.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.66-68
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• 1997

In this paper, our interest is the identification and control of nonlinear dynamic plant, induction motor, by using neural networks. We usually use vector control in the induction motor such as in the DC motor. When we go over the inputs of voltage source invertor, we can find that torque current and flux current couple each other in the induction motor. Before putting control inputs in the system, we should remove the coupling terms which we already know from them. But we should consider that cross coupling terms have time-varying variables. In this paper, we identified the parameter of induction motor by using neural networks and designed the controller with identified parameters. Through this procedure we obtained compensated inputs which are decoupled each other. Using induction motor currents control, we can make the d axis current hold constant value and control the q axis current at the same time.