• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.566-569
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• 2005

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using artificial intelligent(AI) controller. The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using learning mechanism fuzzy neural network(LM-FNN) and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also. this paper is proposed the experimental results to verify the effectiveness of AI controller.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.2 s.233
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• pp.224-231
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• 2005

The present study investigates flow characteristics in an optical disc drive (ODD). Detailed knowledge of the flow characteristics is essential to analyze flow-induced noise and vibration, forced convection and flow friction loss. The ODD used in a personal computer is used for the experiment and rotating velocity of disc is under the 4500 rpm. Time-resolved velocity component and velocity spectrum are obtained using the laser Doppler anemometry (LDA), and the flow patterns induced by rotating disc in the ODD are calculated by a commercial finite volume method at the same time. The results show that the front holes reduce flow-induced noise and the position of pickup body only affects flow near the window. Furthermore, it is possible for cooling of heat sources in the drive through measuring the flow fields under the tray. In addition, the numerical results are well matched up to the experimental results, therefore, the validation of the numerical results can be achieved. From the validation of numerical results, it is possible to predict the flow characteristics of the region where it is unable to conduct the experiment.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.911-916
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• 2001

Structural acoustic modification method based on the structural mobility analysis is applied to reduce the structure-borne noise radiated from hard disk drive system. Sound intensity techniques and ODS(Operational Deflection Shape) techniques are also used in order to provide the structural acoustic information for the mobility modification. The sound intensity is for the acoustic visualization of the noise source locations, and the ODS is for the visualization of the vibration pattern and its dynamic characteristics of the noise sources. Using visualization information of sound and vibration, local structural input mobility is reduced in the frequency band of interest by designing asymmetrical wave-stringer structure in the wave-number domain as well as frequency domain. The overall sound pressure level is reduced by 4dB and its controlled sound power radiated from the disk drive is proved to under 2.8Bel in idle-spinning mode and 3.1 Bel in random-seeking mode, which are the lowest noise levels in the hard disk drive industry.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.6
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• pp.879-884
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• 2002

There are screw, reciprocating and turbo compressor by structure in an air compressor, essential equipment on he industrial spot. Resently it is wide that the range of turbo compressor's use in gradual, turbo compressor needs high speed rotation of impeller in structure, high rated gearbox and conventional induction motor. This mechanical system increased the moment of inertia and mechanical friction loss. Resently the study of turbo compressor applied super high speed motor and drive, removing gearbox made its size small and mechanical friction loss minimum. In this study we tried to develope variable super high speed motor drive systems for 150Hp, 70,000rpm drect drive Turbo compressor. The result of study is applied to a 150Hp direct turbo compressor and makes it goods.

• Journal of Power Electronics
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• v.12 no.3
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• pp.468-476
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• 2012

The interior permanent magnet synchronous motor (IPMSM) has been widely used in electric vehicle applications due to its excellent power to weigh ratio. This paper proposes the maximum torque control of an IPMSM drive using an adaptive learning (AL) fuzzy neural network (FNN) and an artificial neural network (ANN). This control method is applicable over the entire speed range while taking into consideration the limits of the inverter's rated current and voltage. This maximum torque control is an executed control through an optimal d-axis current that is calculated according to the operating conditions. This paper proposes a novel technique for the high performance speed control of an IPMSM using AL-FNN and ANN. The AL-FNN is a control algorithm that is a combination of adaptive control and a FNN. This control algorithm has a powerful numerical processing capability and a high adaptability. In addition, this paper proposes the speed control of an IPMSM using an AL-FNN, the estimation of speed using an ANN and a maximum torque control using the optimal d-axis current according to the operating conditions. The proposed control algorithm is applied to an IPMSM drive system. This paper demonstrates the validity of the proposed algorithms through result analysis based on experiments under various operating conditions.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.2
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• pp.89-97
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network(LM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_{d}$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using LM-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using LM-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.10
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• pp.27-34
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• 2006

This paper proposes a new method of on-line estimation for rotor resistance of the induction motor in the indirect vector controlled drive, using artificial neural network (ANN). The back propagation algorithm is used for training of the neural networks. The error between the desired state variable of an induction motor and actual state variable of a neural network model is back propagated to adjust the weight of a neural network model, so that the actual state variable tracks the desired value. The performance of rotor resistance estimator and torque and flux responses of drive, together with these estimators, are investigated variations rotor resistance from their nominal values. The rotor resistance are estimated analytically, using the proposed ANN in a vector controlled induction motor drive.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.11
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• pp.1174-1182
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• 1990

A hgih efficiency and good dynamic performance drive system of an induction motor is presented in this paper using vector control technique. If the induction motor is driven under light loads with rated flux, the iron loss is excessively large compared with the copper loss, resulting in poor motor efficiency. High efficiency drive of an induction motor can be achieved by adjusting the flux level which leads the total motor loss to be a minimum value. Generally reducing the flux degrades the dynamic performance, but the dynamic performance of the proposed system is also maintained high. If the d-axis is coincident with rotor flux phasor in synchronous rotating reference frame, the stator current can be decoupled as flux component and torque component. At steady state, the developed motor torque is proportional to the product of the flux and torque component. The combination of the two components minimizing the motor loss could be found with numerical method. As the procedure to obtain the optimal combination is too hard, it is found experimentally. The system block diagram is suggested for maximum efficiency control. The proposed system is studied through digital simulation and verified with experiment. The experimental results show the possiblity of a high efficiency drive with good dynamic performance of maximum efficiency control.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.5
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• pp.16-28
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• 2010

This paper proposes maximum torque control of SynRM drive using artificial intelligent(AI)PI and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal axis current for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled AIPI and ANN controller and the operating characteristics controlled by maximum torque control are examined in detail.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.3
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• pp.110-114
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. In this paper maximum torque control of IPMSM drive using artificial intelligent(AI) controller is proposed. The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using adaptive learning mechanism fuzzy neural network(ALM-FNN) and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the experimental results to verify the effectiveness of AI controller.