• 전력전자학회논문지
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• 제9권1호
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• pp.42-49
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• 2004

본 논문은 데드비트 외란 관측기를 사용한 외부 부하 외란 보상과 파라미터 추정기에 의한 보상 이득의 조정을 나타내고 있다. 결론적으로 PMSM의 응답은 지표 시스템을 따른다. 부하 토크 보상 방법은 데드비트 관측기로 구성된다. 노이즈 영향을 감소시키기 위해 MA 처리에 의해 구현된 후단 필터를 적용하였고, RLSM 파라미터 추정기를 가진 파라미터 보상기가 주어진 실제 시스템의 이득 계산시 사용된 파라미터로 가상 동작하여 이득이 오차가 없는 것처럼 동작하게 한다. 제안된 추정기는 문제를 풀기 위해 고성능 외란 관측기와 조합하여 사용한다. 제안된 제어 시스템은 부하토크와 파라미터 변화에 대해 강인하고 정밀한 시스템이 된다. 이상의 제안된 시스템의 안정성과 유용함이 컴퓨터 시뮬레이션과 실험을 통하여 확인되었다.

• 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.

• 한국산학기술학회논문지
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• 제16권12호
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• pp.8187-8194
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• 2015

고 중량의 궤도열차를 1G 포스이상 가속하기 위해서는 많은 추진력이 필요하다. 선형동기전동기(LSM)는 이러한 고 추력이 필요한 시스템에 적합하다. LSM의 추진 효율을 높이기 위해서 추진제어시스템은 정밀한 위상제어를 위한 실시간의 정확한 차량위치정보가 필요하다. 그러나 추진제어시스템은 전자석 길이구간마다 트랙에 설치되어져 있는 홀센서로부터 상대적으로 긴 시간간격을 갖는 불연속 위치정보를 수신하게 된다. 본 논문에서는 기존 d-q 방정식을 이용한 기본 전동기 모델을 구성하였다. LSM에 의해 추진되는 궤도열차의 모터모델은 코깅력과 마찰손실을 반영한 동적모델이다. 그리고 궤도열차용 LSM제어를 위해 차량위치 추정기를 기반으로 하는 추진제어 시스템을 제안하였다. 해당 궤도열차의 모터모델을 기반으로 위치관측기를 포함한 토크제어시스템의 특성을 확인하기 위해서 시뮬레이션을 수행하였다. 시뮬레이션 결과는 본 제어시스템이 선형화된 시뮬레이션 모델 분석으로부터 제어기의 대역폭과 위상여유가 충분하다는 것을 보여주고 있으며, 제시한 위치 추정기 기반의 추력제어 알고리즘이 궤도열차의 추력을 제어하는데 효과적임을 확인시켜주었다. 따라서 위치 관측기의 가용성도 확인 할 수 있었다.

• 전력전자학회논문지
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• 제7권6호
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• pp.517-523
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• 2002

영구자석 동기전동기의 전동기상수 변동을 보상한 센서리스 제어를 제안한다. 실제시스템의 d축 및 q축 전류와 위치 예측기의 전동기 모델의 d축 및 q축 전류의 차이를 이용하여 회전자의 위치를 예측한다. 전동기가 회전방향을 바꿀 때 발생하는 낮은 속도에서 고정자 저항을 검출하고 온도변화에 의한 고정자 저항 및 유기전압 상수의 변동을 보상한다. 또한 회전자 위치 예측기의 이득은 전동기 속도에 따라 보상된다.

• 제어로봇시스템학회논문지
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• 제7권2호
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• pp.125-130
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• 2001

This paper presents an indirect field-oriented (IFO) induction motor position servo drives which uses the model following adaptive controller with the artificial neural network(ANN)-based rotor resistance estimator. The model reference adaptive system(MRAS)-based 2-layer ANN estimates the rotor resistance on-line and a linear model-following position controller is designed by using the estimated the rotor resistance value. At the end, a fuzzy logic system(FLS) is added to make the position controller robust to the external disturbances and the parameter variations. The simulation results show the effectiveness of the proposed method.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.199-204
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• 2006

In this paper, we propose an adaptive control algorithm to improve the position accuracy and reduce the nonlinear friction effects for linear motion servo system. Especially, the considered system includes not only the variation of the mass of the mover but also the friction change by the normal force. To adapt to these problems, we designed the controller with the mass estimator and the compensator by observing the variation of normal force. Finally, the numerical simulation results are presented in order to show the effectiveness of the proposed method to improve the position accuracy compared to other control methods.

• Journal of Hydrospace Technology
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• 제1권1호
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• pp.75-88
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• 1995

Auto-Pilot System uses heading angle information via the position sensor and the rudder device to control the ship's direction. Most of the control logics are composed of the state estimation and control algorithms assuming that the measurement device and the actuator have no fault except the measurement noise. But such asumptions could bring the danger in real situation. For example, if the heading angle measuring device is out of order the control action based on those false position information could bring serious safety problem. In this study, the control system including improved method for processing the position information is applied to the Auto-Pilot System. To show the difference between general state estimator and F.D.F., BJDFs for the sensor and the actuator failure detection are designed and the performance are tested. And it is shown that bias error in sensor could be detected by state-augmented estimator. So the residual confined in the 2-dimension in the presence of the sensor failure could be unidirectional in output space and bias sensor error is much easier to be detected.