• 조명전기설비학회논문지
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• 제26권12호
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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.

• 조명전기설비학회논문지
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• 제24권6호
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• pp.123-130
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• 2010

일반적으로 매입형 영구자석 동기전동기(IPMSM)의 초기설계는 자기등가회로법을 이용한다. IPMSM의 경우 회전자에 매입된 영구자석 배치의 자유도가 매우 높지만 자기등가회로법은 영구자석의 배치를 고려하기 어렵다. 따라서 설계변수와 응답에 대한 관측 자료로부터 해석적인 근사모형을 제시함으로써 실질적인 목적함수를 쉽게 만들 수 있는 반응표면법(RSM)을 활용하였으며 조건에 따른 관계를 예측하기 위해 필요한 실험 데이터는 유한요소법을 이용하였다. 본 논문에서는 IPMSM의 고 토크와 저 토크 리플을 위한 반응 표면법을 이용한 매입형 영구자석 동기전동기의 이층 영구자석 회전자 구조 다목적 최적 설계를 제안한다.

• 전력전자학회논문지
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• 제17권3호
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• pp.213-221
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• 2012

In a PWM inverter-fed IPMSM (Interior Permanent Magnet Synchronous Motor) drive, a dead time is inserted to prevent a breakdown of switching device caused by the short-circuit of DC link. This distorts the inverter output voltage resulting in a current distortion and torque ripple. In addition to the dead time, nonlinearity exists in switching devices of the PWM inverter, which is generally dependent on operating conditions such as the temperature, DC link voltage, and current. The voltage disturbance caused by the dead time and inverter nonlinearity directly influences on the inverter output performance, and it is known to be more severe at low speed. In this paper, a new compensation scheme for the dead time and inverter nonlinearity under the parameter variation is proposed for a PWM inverter-fed IPMSM drive. The overall system is implemented using DSP TMS320F28335 and the validity of the proposed algorithm is verified through the simulation and experiments.

• 한국정밀공학회지
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• 제31권2호
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• pp.105-112
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• 2014

This paper proposes a novel $H{\infty}$ T-S Fuzzy controller with a weighted integral action for Interior Permanent Magnet Synchronous Motor(IPMSM) which have nonlinear dynamics. The $H{\infty}$ T-S Fuzzy controller is used for the robustness of nonlinear systems and the weighted integral action is used for the tracking problem and the improvement of control performance. A T-S Fuzzy controller is designed by combining the local controllers with the overall stability, and LMI(Linear Matrix Inequality)is used to determine the gains of linear controllers. The tracking problem of IPMSM is changed into regulator problem by introducing the integral action and the weighting factor gives flexibility to a $H{\infty}$ fuzzy controller.

• Journal of Power Electronics
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• 제12권3호
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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.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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• pp.752-760
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• 2018

In this paper, interior permanent magnet synchronous motor (IPMSM) design for sensorless drive, considering magnetic neutral point shift according to magnetic saturation, has been proposed. Sensorless control was divided into a method based on inductance and a method based on back induced voltage. Because induced voltage is very small at zero or low speed, error in rotor initial position estimation may occur. Using the ratio of saliency addresses this problem. When using high-frequency injections at low speed, the rotor's initial position is estimated at the smallest portion of the inductance. IPMSM has the minimum inductance at the d-axis. However, if magnetic saturation leads to magnetic neutral point variation, following the load current change, there is a change in the minimum point of inductance. In this case, it can lead to failure of initial rotor position estimation. As a result, it is essential that the blocking design has an inductance minimum point shift. As such, in this study, an IPMSM design method, by blocking magnetic neutral point change, has been proposed. After determining the inductance profile based on the finite element analysis (FEA), the results of proposed method were verified.

• Journal of Electrical Engineering and Technology
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• 제13권1호
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• pp.451-459
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• 2018

This work presents an approach for modeling of electric vehicle considering the vehicle dynamics, drive train, rotational wheel and load dynamics. The system is composed of IPMSM (Interior Permanent Magnet Synchronous Motor) coupled with the wheels through a drive train. Generally, IPMSM is controlled by ordinary PID controllers. Performance of the ordinary PID controller is not satisfactory owing to the difficulties of optimal gain selections. To overcome this problem, a new type of fuzzy logic gain tuner for PID controllers of IPMSM is required. Therefore, in this paper fuzzy logic based gain tuning method for PID controller is proposed and compared with some previous control techniques for the better performance of electric vehicle with an optimal balance of acceleration, speed, travelling range, improved controller quality and response. The model was developed in MATLAB/Simulink, simulations were carried out and results were observed. The simulation results have proved that the proposed control system works well to remove the transient oscillations and assure better system response in all conditions.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 춘계학술대회 논문집 에너지변화시스템부문
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• pp.225-227
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• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper is proposed artificial intelligent-PI(AIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme. The validity of the proposed controller is verified by results at different dynamic operating conditions.

• 조명전기설비학회논문지
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• 제19권8호
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• pp.85-93
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• 2005

본 논문에서는 고속 드라이브를 위하여 IPMSM의 약계자 영역에서 최대 토크제어를 제시한다. 최대 토크동작을 위하여 최적 d축 전류를 결정하고 이 전류를 각 제어모드에서 사용한다. 최대 토크를 발생하기 위하여 전류 조절기의 출력인 인버터의 출력전압은 DC 링크전압을 최대로 이용한다. 제어모드의 원활한 전이는 지령신호에 기초하여 자동적으로 수행한다. 본 논문에서 제시한 최대 토크제어로 IPMSM 드라이브에 적용시험을 한다. 그리고 시험결과의 응답특성을 다양하게 분석하여 본 논문의 타당성을 입증한다.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2009년도 춘계학술대회 논문집
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• pp.420-423
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• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper is proposed hybrid intelligent-PI(HIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme. The validity of the proposed controller is verified by results at different dynamic operating conditions.