• 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.

• 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.

• Journal of Electrical Engineering and Technology
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• v.8 no.1
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• pp.97-105
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• 2013

The control method of the bidirectional DC/DC converter for instantaneous regenerative current control is described in this paper. The general method to control the DC/DC converter is the output voltage control. However, the regenerative current cannot be controlled to be constant with this control method. To improve the performance of the conventional control method, the DC-link voltage of the inverter is controlled within the tolerance range by the instantaneous boost and buck operations of the bidirectional DC/DC converter. By the proposed control method, the battery current can be controlled to be constant regardless of the motor speed variation. The improved performance of the DC/DC converter controlled by the proposed control method is verified by the experiment and simulation of the system with the inverter and IPMSM(Interior Permanent Magnet Synchronous Motor) which is operated by the reduced practical speed profile.

• 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.

• 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.

• 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.

• The Transactions of the Korean Institute of Power Electronics
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• v.22 no.2
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• pp.175-180
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• 2017

The measurement of three-phase current is important to control the instantaneous torque of a interior permanent magnet synchronous motor(IPMSM) using a three-phase inverter. Therefore, shunt resistors are used in low-cost motor-driving systems to measure three-phase current instead of additional current sensors that are too expensive for these systems. However, in certain regions of a space vector plane, shunt resistors cannot reconstruct three-phase current in high-speed driving mode. In this paper, predictive current control is used to compensate for the three-phase current in those regions, which results in a reduction of current ripple in a three-shunt sensing inverter(TSSI) and torque ripple in IPMSM.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.9
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• pp.14-25
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• 2011

The solar vehicle is in the spotlight in the eco-friendly aspect of photovoltaic system using unlimited solar energy. The solar vehicle uses energy of photovoltaic and battery. The solar vehicle uses stored energy in battery when photovoltaic power is lower than consumption power by solar vehicle and if photovoltaic power is higher than consumption power by solar vehicle then photovoltaic power is stored to battery. To improve use efficiency of photovoltaic, the researches about MPPT method to operate maximum power point and interior permanent magnet synchronous motor(IPMSM)drive system using photovoltaic is necessary. This paper proposes MPPT control algorithm for solar vehicle using new fuzzy control(NFC). In this paper, to reduce switching loss, the DC-DC converter is omitted. The NFC controller can be use instead of PO. The NFC controller is performed MPPT control using solar cell voltage and q -axis current of IPMSM. The output of NFC is command q -axis current of IPMSM and this current is operated IPMSM. The response characteristics of algorithm proposed in this paper is compared response characteristics of conventional PO method by PSIM program and validity of this paper prove using this result.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.37-38
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• 2011

본 논문에서는 RT(Real Time)기반의 IPMSM(Interior Permanent Magnet Synchronous Motor) 설계 및 해석, 평가 플랫폼을 개발하고자한다. GUI(Graphical User Interface)로 제작 된 플랫폼 실행창에서 설계변수의 수치적 입력을 통해 설계 자동화를 구현하며, 수치해석프로그램 FEMM과 연동함으로써 전동의 전자계 해석 및 제어정수의 추출을 수행한다. 아울러 추출된 제어정수를 이용, Simulink의 실시간 시뮬레이션을 통해 IPMSM의 제어를 수행하여 통합적 설계 해석 평가를 도출한다.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.310-313
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• 2003

This paper is proposed new fuzzy controller for high performance of interior permanent magnet synchronous motor (IPMSM) drive New fuzzy controller take out appropriate amounts of accumulated control input according to fuzzy described situations in addition to the incremental control input calculated by conventional direct fuzzy controller. The structures of the proposed controller is motivated by the problems of direct fuzzy controller. The direct controller generally give inevitable overshoot when one tries to reduce rise time of response especially when a system of order higher than one is under consideration. The undesirable characteristics of the direct fuzzy controller are caused by integrating operation of the controller, even though the integrator itself is introduced to overcome steady state error in response. Proposed controller fuzzy clear out integrated quantities according to situation. This paper attempts to provide a thorough comparative insight into the behavior of IPMSM drive with direct and new fuzzy speed controller. The validity of the comparative results is confirmed by simulation results for IPMSM drive system.