• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.578-585
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• 2018

This paper proposes a design method for a 3-phase interior permanent magnet synchronous motor (IPMSM) and clamping force control method for an electro-mechanical brake (EMB) using co-simulation for a high-speed train (HST). A traditional pneumatic brake system needs much space for the compressor, brake reservoir, and air pipe. However, an EMB system uses up to 50% less space due to the use of a motor and electric wires for controlling the brake caliper. In addition, it can reduce the latency time for brake control because of the fast response and precise control. A train that has many brakes is advantageous for safety because of the control by sharing the braking force. In this paper, a driving method for a cam-shaft-type EMB is modeled. It is different from the ball-screw-type brakes that are widely used in automobiles. In addition, a co-simulation method is proposed using JMAG and Matlab/Simulink. The IPMSM was designed and analyzed with the JMAG tool, and the control system was simulated using Matlab/Simulink. The effectiveness of the co-simulation results of the mechanical clamping force and braking force was verified by comparison with the clamping force specifications of a HEMU-430X HST.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.309-314
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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. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md 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 adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. 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 adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

• Journal of IKEEE
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• v.26 no.4
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• pp.653-658
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• 2022

This paper presents a Maximum Torque per Ampere (MTPA) control algorithm for an interior permanent magnet synchronous motor (IPMSM) drive considering the permanent magnet (PM) flux linkage variations due to PM temperature variation. PM flux linkage are estimated in real time via a Gopinath style stator flux linkage observer and a torque error correction factor is calculated from the estimated PM flux linkage. A 2-dimensional (2D) MTPA look-up table (LUT) is developed to achieve the MTPA trajectory reflecting PM flux linkage variation for compensating torque error occurred by parameter variation. The proposed IPMSM control algorithm is verified through simulations.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.950-954
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• 1998

This paper describes a development of traction unit for 2-motor driven electric vehicle (EV). The traction unit is consisted with an interior permanent magnet synchronous motor (IPMSM), a reduction gear and an inverter for electric vehicle that is driven by 2 motors without differential gear. For traction unit, prototype IPMSM and inverter have been developed. The IPMSM was designed by CAD program that was developed with both equivalent circuit method and FEM. Also the inverter was developed to drive 2 motors with 6 legs IGBT switches in a control board. The vector control algorithm was implemented with maximum torque control method in the constant torque region and field weakening control method in the constant power region considering inverter capacity. To verify that the traction unit is more high efficiency and has more high power density than a traction unit with induction motor with the same power, we would like to show the results about the design and analysis of the IPMSM and the experiment results about the traction unit.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.709-710
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• 2008

In this paper, we calculated permanent magnetic linkage flux ${\psi}_{\alpha}$ and Ld, Lq parameters of IPMSM and compared two model which has different barrier width. IPMSM has two kinds of torque that reluctance torque and magnetic torque. In constant torque region, using the Maxwell stress tensor method, we calculated the torque and current phase angle ${\beta}$ which has appeared maximum torque. In weakening flux region, we calculated the current phase angle ${\beta}$ which flux ${\psi}_o$ lower than limited flux ${\psi}_{omax}$. From the current phase angle ${\beta}$, we calculated torque by torque equation and compared two model characteristic.

• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.4
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• pp.367-375
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• 2013

The mechanical vibration of a PM synchronous motor at low speeds due to the back emf harmonics may be serious problems in some application such as MDPS(Motor driven power steering), electric vehicles. In this paper, torque ripple reduction for an interior PM synchronous motor including back emf harmonics is proposed. The dq flux linkage harmonics of the permanent magnet are estimated on real time by using the dq currents of the real system and the model of the MRAS observer. Based on the estimated flux linkage harmonics, the dq harmonic currents for reducing the torque ripples are compensated on the dq reference currents. The estimation of the flux linkage harmonics by the MRAS observer and the torque ripple reduction of the proposed algorithm was verified by the simulation and experiment.

• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.146-148
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications. This paper proposes efficiency optimization control of IPMSM drive using AFLC-FNN(Adaptive Fuzzy Learning Control Fuzzy Neural Network)controller. In order to maximize the efficiency in such applications, this paper proposes the optimal control method of the armature current. The optimal current can be decided according to the operating speed and the load conditions. This paper proposes speed control of IPMSM using AFLC-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 AFLC-FNN controller, the operating characteristics controlled by efficiency optimization control are examined in detail.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.9
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• pp.1700-1707
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• 2011

Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. Since the fuzzy neural network(FNN) is recognized general approximate method to control non-linearities and uncertainties, the development of FNN control systems have also grown rapidly. The FNN controller is compounded of fuzzy and neural network. It has an advantage that is the robustness of fuzzy control and the ability to adapt of neural network. However, the FNN has static problem due to their feed-forward network structure. This paper proposes high performance speed control of IPMSM drive using the recurrent FNN(RFNN) which improved conventional FNN controller. The RFNN has excellent dynamic response characteristics because of it has internally feed-back structure. Also, this paper proposes speed estimation of IPMSM drive using ANN. The proposed method is analyzed and compared to conventional FNN controller in various operating condition such as parameter variation, steady and transient states etc.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.3
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• pp.39-46
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• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for the speed control of interior permanent magnet synchronous motor(IPMSM) drive. The design of this algorithm based on FNN controller that is implemented by using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights among the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strongly high performance and robustness in parameter variation, steady-state accuracy and transient response.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.880_881
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• 2009

This paper improves the calculating method about the inductance with the high order frequency coreloss. It is different in which the analyzed inductance for calculating IPMSM and the measured thing for experiment. Due to this phenomenon, the expected input voltage differs from the inquired input voltage for operating the motor. This results from the coreloss margin which have both the 1st order and high order frequency value. For reducing the inductance error, after calculating the equivalence coreloss resistance with having the 1st order frequency Back_EMF and coreloss, designing the inductance with the high order frequency which occurred by the coreloss of high order frequency, and comparing the renovated inductance analysis value with the measured thing.