• Journal of Electrical Engineering and Technology
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• 제11권3호
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• pp.769-774
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• 2016

In the design of sensorless control system for induction motor, high-precision speed estimation is one of the most difficult problems. To solve this problem, the common method is model reference adaptive method (MRAS). MRAS requires accurate motor parameters to estimate rotor speed precisely. However, when motor is running, the variety of temperature and magnetic saturation will lead to the change of motor parameters such as stator resistance and rotor resistance, which will lower the accuracy of the speed estimation. To improve the accuracy and rapidity of speed estimation, this paper analyses the mutual MRAS speed identification based on rotor flux linkage, and proposes an improved mutual MRAS speed identification based on back-EMF. The improved method is verified by Simulink simulation and motor experimental platform based on DSP2812. The results of simulation and experiment indicate that the method proposed by this paper can significantly improve the accuracy of speed identification, and speed up the response of identification.

• 조명전기설비학회논문지
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• 제23권8호
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• pp.33-40
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• 2009

본 논문에서는 HAI(Hybrid Artificial Intelligent) 제어기반의 SV PWM 방식을 이용한 IPMSM의 고성능 제어를 제시한다. HAI 제어기는 적응 퍼지제어 및 신경회로망의 장점을 혼합 적용한다. SV PWM 방식은 지금까지 산업용 전동기 제어분야에 적용되고 있고 출력전류의 고조파 비율, 스위칭 주파수 및 응답특성을 향상시키는 수 있는 기법이다. HAI 제어기는 지령전압을 계산할 때 발생되는 문제점을 해결하기 위하여 종래의 PI 제어기를 대체하여 사용한다. HAI 제어기는 지령모델 기반의 적응제어, 퍼지제어 및 신경회로망으로 구성되어 속도 성능을 개선한다. 본 논문에서는 제시한 HAI 제어기를 적용하여 파라미터 변동, 정상상태 및 과도상태 등의 응답특성을 분석하고 종래의 FNN 제어기 및 PI 제어기의 응답특성과 비교한다. 따라서 본 논문에서는 HAI 제어기의 타당성을 입증한다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2002년도 전력전자학술대회 논문집
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• pp.213-216
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• 2002

This paper describes a new approach to estimate induction motor speed from terminal voltages and currents for speed-sensorless vector control. This algorithm is based on Model Reference Adaptive System(MRAS). The proposed technique is simple and robust to the variation of motor parameters. Specially, this algorithm is not affected by the variation of stator resistance and it does not require any pure integration at all. The validity of this new approach is proved by simulations.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2003년도 추계학술대회 논문집
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• pp.186-189
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• 2003

This paper is proposed to position and speed control of interior permanent magnet synchronous motor(IPMSM) drive without mechanical sensor. A gopinath observer is used for the mechanical state estimation of the motor. The observer was developed based on nonlinear model of IPMSM, that employs a d-q rotating reference frame attached to the rotor, A gopinath observer is implemented to compute the speed and position feedback signal. The validity of the proposed scheme is confirmed by various response characteristics.

• 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.

• 한국항행학회논문지
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• 제16권6호
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• pp.1123-1133
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• 2012

논문에서는 극저속 영역 및 저속 영역에서 안정적이고 동특성이 우수한 벡터제어 시스템을 구성하여, 축소차원 상태관측기를 이용한 순시속도 관측기와 극 저속 제어, 회전자 속도를 추정하는 벡터제어 시스템에 관한 방법을 제안하였다. 본 시스템에서 제안된 관측기는 축소차원 상태관측기를 부하토크 추정에 적용하여 속도추정에 이용함으로서 시스템구성을 간단히 구현 하면서도 극 저속 영역에서 정확한 순시속도 추정이 가능하였다. 또한, 시스템 잡음에 의한 영향을 줄이고, 관측기의 극을 변화시키는 일 없이 부하외란 이나 모델화 오차, 측정 잡음 등에 강인한 유도전동기 속도제어 시스템을 제시하였다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2004년도 전력전자학술대회 논문집(1)
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• pp.42-45
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• 2004

This paper is proposed an adaptive fuzzy controller of induction motor drive. The adaptive fuzzy controller approach for an estimate of the rotor time constant which is used to adjust the estimate of the slip angular speed. An estimate of the rotor time constant was obtained using an model reference adaptive system(MRAS) in a fuzzy control scheme. The rotor time constant was estimated by utilizing the rotor nut estimates. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 B
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• pp.1165-1168
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for sensorless vector controlled induction motor drives. MRAS(Model Reference Adaptive System) is used for rotor speed estimation. When induction motor is continuously used long time. its electrical and mechanical parameters will change, which degrade the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. The proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using DS1102 board show the robustness of the proposed controller to parameter variations.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.1113-1114
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• 2011

This paper is proposed design of high performance controller of SynRM drive using FNN and NNC. Also, This paper is proposed of designing fuzzy neural network controller(FNNC) which adopts the fuzzy logic to the artificial neural network(ANN). FNNC combines the capability of fuzzy reasoning in handling uncertain information and the capability of neural network in learning from processes. This controller is controlled speed using FNNC and model reference adaptive fuzzy control(MFC), and estimation of speed using ANN. The performance of proposed controller was demonstrated through response results. The results confirm that the proposed controller is high performance and robust under the variation of load torque and parameters.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 A
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• pp.574-577
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• 1996

In order to reduce the torque pulsation caused by non-sinusoidal flux distribution of the brushless direct drive motor(BLDDM), a new torque control technique is proposed. The linkage flux of the BLDDM is first estimated by using the model reference adaptive system (MRAS) technique and the instantaneous torque of the BLDDM is then estimated from the mathematical model including this estimated linkage flux. By using the estimated instantaneous torque of the BLDDM, the minor torque control loop to suppress the undesirable torque pulsation is designed. To show the effectiveness of the proposed control scheme, the simulations and experiments are carried out for the DSP-based BLDDM drive system with a power rate of 120W. It is well demonstrated from these results that the torque and speed control performance of the BLDDM is much improved by employing the proposed control scheme.