• 전기학회논문지
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• 제65권8호
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• pp.1362-1368
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• 2016

This paper proposes the improved transition scheme for a sensorless drive of an interior permanent magnet synchronous motor (IPMSM). In order to operate the IPMSM, the current controller can be used until 300 rpm for the initial operation. After that, the control method of IPMSM is changed to the speed controller for the sensorless control method. At that point, the rotor speed overshoot is generated due to the rapid change of the current reference for the speed controller. The proposed algorithm is able to reduce the overshoot of a rotor speed by compensating the estimated feedforward value to the speed controller. The feedforward value of the current reference is estimated by using a coordinate transformation and is approximated to the current reference after the transition of the control mode. The effectiveness of the proposed scheme is verified by experiments using an IPMSM drive system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 B
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• pp.1453-1455
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• 2005

This paper presents self tuning PI controller of IPMSM drive using neural network. Self tuning PI 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 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.

• 전력전자학회논문지
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• 제17권5호
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• pp.401-408
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• 2012

A simple on-line elimination strategy of the dead time and inverter nonlinearity using the current slope information is presented for a PWM inverter-fed IPMSM (Interior Permanent Magnet Synchronous Motor) drive. In a PWM inverter-fed IPMSM drive, a dead time is inserted to prevent a breakdown of switching device. This distorts the inverter output voltage, resulting in a current distortion and torque ripple. In addition to the dead time, inverter 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 proposed scheme is based on the fact that the d-axis current ripple is mainly caused by the dead time and inverter nonlinearity. To eliminate such an influence, the current slope information is determined. The obtained current slope information is processed by the PI controller to estimate the disturbance caused by the dead time and inverter nonlinearity. The overall system is implemented using DSP TMS320F28335 and the validity of the proposed algorithm is verified through the simulation and experiments. Without requiring any additional hardware, the proposed scheme can effectively eliminate the dead time and inverter nonlinearity even in the presence of the parameter uncertainty.

• 전기학회논문지
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• 제60권2호
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• pp.293-300
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• 2011

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance and adaptive fuzzy learning contrroller(AFLC) for speed control in IPMSM Drives. AFLC is chaged fuzzy rule base by rule base modifier for robust control of IPMSM. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator and AFLC is confirmed by comparing to conventional algorithm.

• Journal of Power Electronics
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• 제9권1호
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• pp.117-123
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• 2009

This paper presents a sliding mode observer for the sensorless control of interior permanent magnet synchronous motor (IPMSM) drives. The sliding mode observer has been presented as a robust estimation method. Most of these previous works, however, were not for an interior PMSM (IPMSM), but for a non-salient pole PMSM and its observer design is conducted in the stationary reference frame. Thus, in this paper, we investigate the design of a sliding mode observer and its driving characteristics for an IPMSM. The proposed sliding mode observer is designed in the rotating reference frame, and good drive performance is achieved even when the observer parameters are mismatched with those of an actual motor. The proposed method is applied to a 600W IPMSM, and, then, the measurement results are presented.

• Journal of Power Electronics
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• 제9권6호
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• pp.960-968
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• 2009

This paper presents a design optimization process for interior permanent magnet synchronous motors (IPMSM) for hybrid electric compressors (HEC) which are applied to hybrid electrical vehicles. A hybrid electric compressor is composed of an electric motor driving section and an engine driving section which is connected to the engine by a pulley belt. A hybrid electric compressor driving motor requires half of the full driving power of a compressor. Even though an engine is not operated at the idling stop mode, the electric motor drives the air-conditioner compressor by itself so that the air conditioning system can produce its minimum cooling capacity. In this paper, the design optimization of an IPMSM for a 42 (V) applied voltage system is studied using the design of experiment (DOE) and response surface method (RSM) of 6sigma. The driving characteristics of this motor drive system are measured and analyzed by experiment.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.126-128
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• 2007

This paper proposes maximum torque control of IPMSM drive using Adaptive Fuzzy-Neuro controller and artificial neural network(ANN). The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. This paper proposes the analysis results to verify the effectiveness of the Adaptive Fuzzy-Neuro and ANN controller.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2003년도 추계학술대회 논문집
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• pp.182-185
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• 2003

This paper combines the adaption of MRAS with the ability of NN for better modeling of nonlinear systen It presents an MRAS using an NN in the adaption mechanism. The technique is applied to a IPMSM drive. The torque constant and stator resistance variations on the speed and position estimations over a wide speed range has been studied. The NN estimators are able to track the varying parameter of different speeds with consistent performance. The validity of the proposed estimator is confirmed by the operating characteristics controlled by neural networks control.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.105-110
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• 2008

This Paper proposes Dual-PI controller for high performance control of IPMSM drive. Input of traditional PI control used speed error, but Dual-PI controller used two input speed error, current error and output is output is f-axis current. Dual-PI controller is Possible both speed control and current control because it used speed error and current error Therefore, dual-PI controller can is reduced current ripple. This paper is made analysis performance of algorithm and proposes result.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권4호
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• pp.220-227
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• 2005

This paper presents hybrid artificial intelligent(HAI) controller based on the vector controlled IPMSM drive system. And it is based on artificial technologies that adaptive neural network fuzzy(A-NNF) is to speed control and artificial neural network(ANN) is to speed estimation. The salient feature of this technique is the HAI controller The hybrid action tolerates any inaccuracies in the fuzzy logic assignment rules or in the neural network stationary weights. Speed estimators using feedforward multilayer and artificial neural network(ANN) are compared. The back-propagation algorithm is easy to derived the estimated speed tracks precisely the actual motor speed. This paper presents the theoretical analysis as well as the simulation results to verify the effectiveness of the new hybrid intelligent control.