• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.780_781
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• 2009

This paper proposes efficiency optimization control of IPMSM drive using hybrid intelligent controller(HIC). The design of the speed controller based on fuzzy-neural network that is implemented using fuzzy control and neural network. The design of the current based on adaptive fuzzy control using model reference and the estimation of the speed based on neural network using ANN 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 ALM-FNN, current control of model reference adaptive fuzzy control(MTC) and estimation of speed using ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled HIC, the operating characteristics controlled by efficiency optimization control are examined in detail.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.12
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• pp.9-20
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• 2012

This paper proposes the efficiency optimization control of interior permanent magnet synchronous motor(IPMSM) drive using series connected-fuzzy neural network PI(SC-FNPI) controller. The PI controller is generally used to control IPMSM drive in industrial field. However, the PI controller has problem which is falling control performance about parameter variation such as command speed, load torque and inertia due to fixed gain of PI controller. Therefore, to improve performance of PI controller, this paper proposes SC-FNPI controller adjusted input of PI controller by FNN controller according to operating conditions. Also, this paper proposes efficiency optimization control which is improving efficiency with minimize loss. The SC-FNPI controller proposed in this paper is compared control performance with conventional FNN and PI controller about command speed, load torque and inertia variation. And the efficiency optimization control is compared with $i_d=0$ control about loss and efficiency. The SC-FNPI controller proposed in this paper shows more excellent control performance for rising time, overshoot and steady-state error. Also efficiency optimization control is increased efficiency by reducing loss.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.2 s.314
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• pp.24-31
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• 2007

This paper presents self tuning PI(STPI) controller of IPMSM drive using neural network. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, STPI controller proposes a new method based neural network. STPI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network 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 results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• 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.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1105-1106
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• 2011

본 논문에서는 EV 구동용 유도전동기와 매입형 영구자석 동기전동기(IPMSM)의 동작특성과 효율을 비교하기 위하여 농형구조 유도전동기와 단층구조 IPMSM을 설계 하였다. EV 구동용 전동기는 정속운전 뿐만 아니라 가변속 운전이 필수적이므로 유도전동기는 V/f 제어, IPMSM은 약계자 제어를 사용하여 가변속 상태에서의 운전토크와 효율을 유한요소법을 이용하여 해석하였다.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.11a
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• pp.313-318
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• 2007

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 fuzzy neural network controller and artificial neural network(ANN). This control method is applicable over the entire speed range which 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 Adaptive-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 reposes speed control of IPMSM using Adaptive-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 a lied to IPMSM drive system controlled Adaptive-FNN and ANN controller, 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-FNN and ANN controller.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1558-1565
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• 2018

Three-phase current is reconstructed from the dc-link current in an AC machine drive with a single current sensor. Switching pattern modification methods, in which the magnitude of the effective voltage vector is secured over its minimum, are investigated to accurately reconstruct the three-phase current. However, the existing methods that modify the switching pattern cause voltage and current distortions that degrade sensorless performance. This paper proposes a variable-magnitude voltage signal injection method based on a high frequency voltage signal injection. The proposed method generates a voltage reference vector that ensures the minimum magnitude of the effective voltage vector by varying the magnitude of the injection signal. This method can realize high quality current reconstruction without switching pattern modification. The proposed method is verified by experiments in a 600W Interior permanent magnet synchronous machine (IPMSM) drive system.

• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.3
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• pp.225-231
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• 2013

This paper presents an improved signal processing technique in the square-wave-type voltage injection method for IPMSM sensorless drives. Since the sensorless method based on the square-wave voltage injection does not use low-pass filters to get an error signal for estimating rotor position and allows the frequency of the injected voltage signal to be high, the sensorless drive system may achieve an enhanced control bandwidth and reduced acoustic noise. However, this sensorless method still requires low-pass and band-pass filters to extract the fundamental component current and the injected frequency component current from the motor current, respectively. In this paper, these filters are replaced by simple arithmetic operations so that the time delay for estimating the rotor position can be effectively reduced to only one current sampling. Hence, the proposed technique can simplify its whole signal process for the IPMSM sensorless control using the square-wave-type voltage injection. The proposed technique is verified by the experiment on the 800W IPMSM drive system.

• 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.

• 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.