• 전력전자학회:학술대회논문집
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• 전력전자학회 1997년도 전력전자학술대회 논문집
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• pp.97-100
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• 1997

A new control method for the robust position control of a brushless DC(BLDC) motor using the asymptotically stable adaptive load torque observer is presented. A precision position control is obtained for the BLDC motor system approximately linearized using the field-orientation method. And the application of the load torque observer is published in [1] using fixed gain. However, the flux linkage is not exactly known for a load torque observer. Therefore, a model reference adaptive observer is considered to overcome the problem of the unknown parameter in this paper. And stability analysis is carried out using Liapunov stability theorem. As a result, asymptotically stable observer gain can be obtained without affecting the overall system response. The load disturbance detected by the asymptotically stable adaptive observer is compensated by feedforwarding the equivalent current having the fast response.

• 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.

• 조명전기설비학회논문지
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• 제28권7호
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• pp.48-54
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• 2014

This paper discusses study of torque ripple minimization employing an improved TDF(torque distribution function)-based instantaneous torque control to reduce acoustic noise and vibration problem of the SRM. As the flux linkage of the SRM is a nonlinear function of phase current and rotor position, design of optimal controller for the SRM is quite complicated. Hence, an accurate mathematical model considering the nonlinearity of the SRM is required. An improved TDF based torque control has been proposed in order to reduce the toque ripple at high speed operation. Dynamic simulation using Matlab/Simulink as well as Finite Element Analysis is presented. A prototype SRM for electric vehicle traction has been manufactured to validate the experimental results comparing the dynamic simulation results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.991-992
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• 2011

This study was designed to satisfy induced voltage limits considering drive's specifications and optimize design reducing usage of permanent magnet, by increasing salient poles ratio, when designing 150kW IPMSM. In order to achieve these objectives, design plans were determined, based on Ld and Lq parameters of a basic design model, according to changes in salient poles ratio and flux linkage using IPMSM's voltage equation and torque equation and then, required torque and induced voltage were analyzed using Sensitivity Analysis. Based on analysis data, the optimum design was performed and basic model's characteristics were compared to final model's through Gradient-Based Optimization Technique.

• 한국산학기술학회논문지
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• 제17권2호
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• pp.697-702
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• 2016

매입형 영구자석 동기전동기의 원활한 제어를 위해서는 전동기의 상수인 고정자 저항이나 각종 인덕턴스의 값, 그리고 쇄교 자속의 값 등을 정확하게 알아야 한다. 그러나 이러한 상수들은 전동기 운행에 따른 전동기 온도의 상승이나 각 작동점 등의 변화에 따라 계속적으로 변하기 때문에 이들의 값을 정확하게 추정하는 것은 매우 어렵다. 이러한 문제점을 극복하기 위해서 실시간으로 상수의 값을 추정하는 기법이 필요한데 본 논문에서는 MRAS(Model Reference Adaptive System) 기법을 이용한 상수의 추정 및 적응제어 기법을 제시한다. 시스템의 관계식이 이들 상수에 대해 비선형으로 구성되어 있어 일반적인 제어기법을 적용하는데 문제가 있어 일부 논문에서는 상수의 일부를 안다고 가정하였지만 본 논문에서는 이러한 가정 없이 모든 상수를 추정하는 제어기법을 제시하고 적응제어 기법의 수렴성을 입증하였다. 제시하는 알고리즘의 우수성은 시뮬레이션을 통해 입증하도록 한다.

• 전기학회논문지P
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• 제64권1호
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• pp.7-13
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• 2015

This paper presents mathematical models for high speed permanent-magnet synchronous machine. The mathematical method with two successive steps is used to estimate design parameter as well as the output power. At first, mathematical model for a linkage flux problem is employed to calculate the number of winding turns and stack length of armature core. The magnetic circuit model for an induced voltage and the electric circuit model for a current are modeled. The output powers of the electrical generator were evaluated by the mathematical techniques. The results of this mathematical methods predict the specifications of the machine and can be applied in the design stage of the electrical machine.

• Journal of Power Electronics
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• 제17권6호
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• pp.1512-1522
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• 2017

Switched reluctance motor (SRM) is suitable for electric vehicle (EV) applications with the advantages of simple structure, good overload capability, and inherent fault-tolerance performance. The SRM dynamic simulation model is built based on torque, voltage, and flux linkage equations. The EV model is built on the basis of the analysis of forces acting on a vehicle. The entire speed range of the SRM drive is then divided into constant torque and constant power areas. The command torque of the motor drive system is given according to the accelerator pedal coefficient and motor operation areas. A novel adaptive variable angle control is proposed to avoid the switching chattering between the current chopping control and angle position control modes in SRM drives for EV applications. Finally, simulation analysis and experimental results are conducted to verify the accuracy of the proposed simulation model and control strategy.