• 전력전자학회논문지
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• 제18권3호
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• pp.289-295
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• 2013

It is important to know exact values of Interior Permanent Magnet Synchronous Motors(IPMSM)' parameters such as stator resistance and inductance in order to have their high performance. This paper proposes a novel motor parameter(stator resistance, d&q axis inductance) estimation algorithm for IPMSM. The proposed estimation method utilizes back-EMF equations and model reference adaptive system(MRAS). The algorithm using back-EMF estimates d and q axis inductances in the constant torque region, and the stator resistance is estimated by using MRAS with the estimated inductance regardless of speed regions. The validity of the proposed algorithm is verified by simulations and experiments.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2013년도 추계학술대회 논문집
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• pp.179-181
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• 2013

일반적인 전동기 구동에 있어서 부하의 변화에 의해 관성이 변하는 경우 동일 속도 제어기 이득에서의 속도 응답은 오버슈트가 생기거나 진동하는 문제점이 있다. 따라서 정확한 속도제어가 필요한 응용분야에서 전동기의 관성 추정은 필수이다. 본 논문에서는 MRAS 관측기를 활용하여 영구자석동기전동기의 관성을 추정하고 추정된 관성으로 속도제어기의 제어이득을 선정할 경우의 속도 응답특성을 비교한다.

• Journal of Power Electronics
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• 제11권6호
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• pp.864-869
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• 2011

This paper presents a sensorless control and parameter estimation strategies for a Transverse Flux Rotating Motor (TFRM). The proposed sensorless control method is based on a Model Reference Adaptive System (MRAS) to estimate the stator flux. Parameter estimation theory is also applied into the sensorless control method to estimate motor parameters, such as inductances. The effectiveness of the proposed methods is verified by some simulations and experiments.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1996년도 창립기념 전력전자학술발표회 논문집
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• pp.1-4
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• 1996

The purpose of this treatise is to estimate speed of an induction motor and realize a robust speed control system with estimated speed in field-weakening region. A speed estimation is based on Model Reference Adaptive System(MRAS) technique and two flux estimator are designed to be robust against parameter variation. The MRAS-based overall control scheme has been implemented on 7.5kW Spindle induction motor control system. And it is verified that the proposed control scheme is very stable and robust in field-weakening region.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 B
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• pp.1004-1005
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• 2006

This paper investigates a speed sensorless control of induction motor. The control strategy is based on MRAS (Model Reference Adaptive System) using load-torque observer as a reference model for flux estimation. The speed response of conventional MRAS controller characteristics is affected by variations of load torque disturbance. In the proposed system, the speed control characteristics using a load-torque observer control isn't affected by a load torque disturbance. Simulation results are presented to prove the effectiveness of the adaptive sliding mode controller for the drive variable load of induction motor.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제48권11호
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• pp.632-639
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• 1999

SRM(Switched Reluctance Motor) drives require the accurate position and speed information of the rotor. These informations are generally provided by a shaft encoder or resolver. High temperature, EMI, and dust may make detection performance deteriorate. Therefore, the elimination of the position and speed sensor is desirable. In this paper, a nonlinear adaptive observer using the MRAS(Model Reference Adaptive System) is proposed. The rotor speed and position are estimated by the adaptation law using the real and estimated currents. The stability of the adaptive observer is proved by Lyapunov stability theory. The proposed methods are implemented with TMS320C31 DSP. Experimental results prove that the observer has a good estimation performance of the rotor speed and position despite of the parameter variations and loads, and the speed control can be accomplished in the wide speed range.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권6호
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• pp.389-394
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• 2004

This paper proposes a speed estimation method using FLC(Fuzzy Logic Controller) in order to realize the speed control of PMAM(Permanent Magnet AC Motor) with no speed sensor. This method uses FLC as a adaptive laws of MRAS(Model Reference Adaptive System) and estimates the rotor speed of PMAM with a difference between the reference model and the adjustable model. Speed control is performed by PI controller with the estimated speed. The experiment is executed to verify the propriety and the effectiveness of the proposed system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.427-431
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• 1996

The MRAS proposed by Schauder [8] is modified to improve robustness to the change of load torque and/or the variation of the stator resistance. The difference between the voltage and the current model is fed into the current model via proportional and integral gains. In order to generalize the MRAS, supposing that the rotor speed is time varying, we add a compensating term to the current model. It does not alter the Popov's integral inequality condition. Also, the asymptotic stability of the modified MRAS (MMRAS) is shown with the stability proof technique as in the original paper. By the simulation works, it is verified that the MMRAS obtains improved performance than the original MRAS.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.660-662
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• 2004

This paper proposes an Intelligent Speed Estimator in order to realize the speed-sensorless vector control of an induction motor. Intelligent Speed Estimator used Model Reference Adaptive System which has Fuzzy-Neural adaptive mechanism as Speed Estimation method. The Intelligent Speed Estimator estimates the speed of an induction motor with a rotor flux of a reference model and adjustable model in MRAS. The Intelligent Speed Estimator reduces the error of the rotor flux between the voltage flux model and the current flux model using the error and the change of error as input of the Estimator. The computer simulation is executed to verify the propriety and the effectiveness of the proposed speed estimator.

• 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.