• Proceedings of the KIPE Conference
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• 2012.11a
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• pp.225-226
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• 2012

본 논문은 스위치드 릴럭턴스 전동기의 순시 토크 맥동 저감 기법을 제안한다. 스위치드 릴럭턴스 전동기는 일반적으로 이중 돌극형의 구조로 인한 토크 맥동과 소음 발생의 단점을 갖는다. 본 논문에서 제안하는 제어 기법은 퍼지 로직 기반의 최적 턴 오프각 제어와 슬라이딩 모드 제어 기반의 토크 지령 보상 기법을 결합하여 순시적으로 발생하는 토크 맥동을 보상한다. 750W급 전동기 모델의 시뮬레이션 결과는 제안하는 제어 기법의 우수성을 보인다.

• Proceedings of the KIEE Conference
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• 2003.10b
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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.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.761-762
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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 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.

• Proceedings of the KIPE Conference
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• 2012.11a
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• pp.223-224
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• 2012

본 논문은 퍼지 기반의 토크 분배 함수를 이용한 스위치드 릴럭턴스 모터의 토크 맥동 저감 기법을 제안한다. 일반적으로 스위치드 릴럭턴스 모터의 토크 맥동 저감 기법은 선행된 실험결과와 모터 파라미터의 변화에 대한 관측을 통해 오프라인으로 토크 분배 함수를 최적화한다. 이때, 모터의 높은 인덕턴스는 전류가 토크 분배 함수를 잘 추종하지 못하게 하여 의도치 않은 토크 맥동을 유발한다. 게다가 오프라인으로 토크 분배 함수를 계산하기 때문에 모델의 오차 및 변화에 따라 보상 성능이 저하 될 수 있다. 제안하는 제어기법은 퍼지 제어기를 이용하여 순시적으로 토크 분배 함수의 형상을 정정함으로써 토크 맥동을 저감한다. 시뮬레이션 결과를 통해 제안하는 제어기법의 우수성을 보인다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.649-651
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• 2015

This paper describes a robust adaptive sliding mode control (RASMC) for torque ripple minimization of switched reluctance motor (SRM) using it in automotive application. The objective is to control effort smoothness while the system is under perturbations by unstructured uncertainties, unknown parameters and external disturbances. The control algorithm employs an adaptive approach to remove the need for prior knowledge within the bound of perturbations. This is suitable for tackling the chattering problem in the sliding motion of sliding mode control method. The algorithm then incorporates modifications in order to build a chattering-free modified robust adaptive sliding mode control using Lyapunov stability theory.

• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.4
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• pp.477-482
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• 2012

This paper describes a new rotor position sensorless control method for SRM drives based on an improved simplified flux linkage method. In the traditional simplified flux linkage method, every phases take turns conduction and current chopping control method is used. Every phases take turns conduction means turning on the incoming working phase while turning off the working phase. This conduction mode causes coupling between turn-on and turn-off angles, which goes against optimal efficiency or torque ripple minimization with sensorless speed control. In the improved simplified flux linkage method, turn-off angle is calculated by flux loop, the turn-on angle can be given arbitrarily and has no relations with the turn-off angle, and the current chopping control method is used. The speed and rotor position can be estimated then. Finally, a sensorless SRM speed control system and an experiment platform with DSP are built and validity of this method is confirmed.