• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제53권5호
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• pp.332-337
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• 2004

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 in IPMSM Drives. 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 is confirmed by the operating characteristics controlled by neural networks control.

• 제어로봇시스템학회논문지
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• 제13권5호
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• pp.429-433
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• 2007

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 ststor resistance in IPMSM Drives. 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 is confirmed by the operating characteristics controlled by neural networks control.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.207-209
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• 2006

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 ststor resistance in IPMSM Drives. 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 is confirmed by the operating characteristics controlled by neural networks control.

• 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.

• 전력전자학회논문지
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• 제10권2호
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• pp.186-193
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• 2005

본 논문에서는 산업체 CNC(Computer Numerical Control) 분야에서 사용되는 빌트인 타입 스핀들 모터를 구동하기 위한 기법을 제시하였다. 저속 영역에서 유리한 전류 모델과 고속 영역에서 유리한 전압 모델을 혼합해서 사용하는 고피나스 모델 자속추정기는 회전자 자속추정을 위하여 이용하였다. 그리고 약계자 제어를 사용하여 스핀들 모터를 고속 운전하였다. 시뮬레이션과 실험을 통해 약계자 영역에서의 스핀들 모터 고속운전을 확인하였다.

• 한국전자통신학회논문지
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• 제16권1호
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• pp.37-44
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• 2021

본 연구는 터렛 서보 시스템에서 사용되는 고가의 절대형 엔코더를 대체하기 위해서 멀티-턴 가능한 센서리스 위치제어기를 구현하였다. 센서리스 제어를 위해서는 모터의 위치 정보가 필수적이다. 따라서, 터렛 서보시스템에서 사용되는 영구자석형 동기 전동기의 수학적 모델로부터 자속 추정기를 구성하였다. 자속 추정기로부터 회전자 자속을 계산하여 회전자의 속도와 위치 정보를 얻었다. 제로-크로싱 기법을 사용하여, 추정한 회전자 자속이 1회전 할 때마다 하나의 펄스를 생성하여 멀티-턴 횟수를 측정하였다. 모의실험과 실험을 통해 제안한 방법의 유용함을 확인하였다.

• 전력전자학회논문지
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• 제18권3호
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• pp.232-239
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• 2013

This paper proposes an improved stator flux estimator through ensuring conventional PLPF to act as a pure integrator for sensorless control of induction motors. Conventional PLPF uses the estimated synchronous speed as a cut-off frequency and has the gain and phase compensators. The gain and phase compensators are determined on the assumption that the estimated synchronous angular speed is coincident with the real speed. Therefore, if the synchronous angular speed is not same as the real speed, the gain and phase compensation will not be appropriate. To overcome the problem of conventional PLPF, this paper analyzes the relationship between the synchronous speed error and the phase lag error of the stator flux. Based on the analysis, this paper proposes the synchronous speed error compensation scheme. To achieve a start-up without speed sensor, the current model is used as the stator flux estimator at the standstill. When the motor starts up, the current model should be switched into the voltage model. So a stable transition between the voltage model and the current model is required. This paper proposes the simple transition method which determines the initial values of the voltage model and the current model at the transition moment. The validity of the proposed schemes is proved through the simulation results and the experimental results.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제27권4호
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• pp.245-271
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• 2023

In this paper we discuss some recovery of H(div)-conforming flux approximations from the equilibrated fluxes of Ainsworth and Oden for quadratic finite element methods of second-order elliptic problems. Combined with the hypercircle method of Prager and Synge, these flux approximations lead to a posteriori error estimators which provide guaranteed upper bounds on the numerical error. Furthermore, we prove some superconvergence results for the flux approximations and asymptotic exactness for the error estimator under proper conditions on the triangulation and the exact solution. The results extend those of the previous paper for linear finite element methods.

• 전기학회논문지
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• 제61권10호
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• pp.1442-1447
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• 2012

In this paper, direct power control system for three-phase PWM AC/DC converter without the source voltage sensors is proposed. The sinusoidal input current and unity effective power factor are realised based on the estimated flux in the observer. Both active and reactive power calculated using estimated flux. The estimation of flux is performed based on the Reduced-order flux observer using the actual currents and the command control voltage. The source voltage sensors are replaced by a flux estimator. The active and reactive powers estimation are performed based on the estimated flux and Phase anble. The proposed algorithm is verified through simulation and experiment.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.197-199
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• 2003

This paper is Proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. 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 is confirmed by the operating characteristics controlled by neural networks control.