• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2007년도 추계학술대회 논문집
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• pp.307-312
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• 2007

The paper is proposed artificial neural network(ANN) sensorless control of induction motor drive with fuzzy learning control-fuzzy neural network(FLC-FNN) controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of induction motor using FLC-FNN and estimation of speed using ANN controller The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled FLC-FNN and ANN controller, Also, this paper is proposed the analysis results to verify the effectiveness of the FLC-FNN and ANN controller.

• Journal of Power Electronics
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• 제12권1호
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• pp.67-74
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• 2012

According to electrical output up rating of a permanent magnet motor and request to operate for a large variable speed range, resonance between structural natural vibration and electromagnetic force inside the motor can take place and make noise. This paper describes the mechanism of a resonance between them and noise lowering procedure by frequency shift when they are applied to the reluctance torque largely employed new motor named Permanent magnet Reluctance Motor (PRM).

• 전기학회논문지
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• 제57권9호
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• pp.1552-1554
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• 2008

Recently, sensorless induction motor drives have been much studied due to several advantages. Sensorless drives eliminate the additional mounting space, increase the reliability in harsh environments, and reduce the cost of a motor. This paper investigates an improved sliding mode observer for the sensorless speed control of an induction motor. The proposed control strategy is the sliding mode observer with a variable boundary layer for a low-chattering and fast-response control. The proposed sensorless-algorithm is verified through the simulation and experimentation.

• 전자공학회논문지SC
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• 제42권6호
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• pp.77-82
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• 2005

본 논문은 FNN과 ANN 제어기를 이용한 유도전동기의 속도 제어 및 추정을 제시한다. 먼저, PI 제어기에서 나타나는 문제점을 해결하기 위하여 퍼지제어와 신경회로망을 혼합 적용한 FN 제어기를 설계한다. 퍼지제어기의 강인성 제어와 신경회로망의 고도의 적응제어의 장점들을 접목한다. 다음은 ANN을 이용하여 유도전동기 드라이브의 속도 추정기법을 제시한다. 2층 구조를 가진 신경회로망에 BPA(Back Propagation Algorithm)를 적용하여 유도전동기 드라이브의 속도를 추정한다. 추정속도의 타당성을 입증하기 위하여 시스템을 구성하여 제어특성을 분석한다. 그리고 추정된 속도를 지령속도와 비교하여 전류제어와 공간벡터 PWM을 통하여 유도전동기의 속도를 제어한다. 본 연구에서 제시한 FNN과 ANN의 제어특성 및 추정성능을 분석하고 그 결과를 제시한다.

• 전력전자학회논문지
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• 제10권3호
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• pp.282-289
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• 2005

본 논문에서는 NFC(Neuro-Fuzzy Controller)와 ANN(Artificial Neural network) 제어기를 이용한 IPMSM의 속도 제어 및 추정을 제시한다. PI 제어기에서 나타나는 문제점을 해결하기 위하여 신경회로망과 퍼지제어를 혼합적용한 NFC를 설계한다. 신경회로망의 고도의 적응제어와 퍼지 제어기의 강인성 제어의 장점들을 접목한다. 다음은 ANN을 이용하여 IPMSM 드라이브의 속도 추정기법을 제시한다. 2층 구조를 가진 신경회로망에 BPA(Back Propagation Algorithm)를 적용하여 IPMSM 드라이브의 속도를 추정한다. 추정속도의 타당성을 입증하기 위하여 시스템을 구성하여 제어특성을 분석한다.

• 제어로봇시스템학회논문지
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• 제2권4호
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• pp.351-361
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• 1996

A variable structure controller is developed for an AC servo motor used in CNC milling machines. The designed controller is implemented as an outer loop controller to a factory designed motor-servopack system. The robustness parameter is tuned for a fast response when the speed tracking error is large, while it is tuned for small oscillations when the speed tracking error is small. The designed controller is installed on a CNC machine using a PC. Cutting experiments show improved performance over the factory-designed controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.782-784
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• 1999

This paper deals with speed control of DC-servo motor using a Back-Propagation(BP) Learning Algorism and a PID controller Conventionally in the industrial control, PID controller has been used. But the PID controller produced suitable parameter of each system and also variable of PID controller should be changed enviroment, disturbance, load. So this paper revealed for experimental, a neural network and a PID controller combined system using developed speed characters of a Variable Load DC-servo motor. The parameters of the plant are determined by neural network perform on on-line system after training the neural network on off-line system.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1998년도 전력전자학술대회 논문집
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• pp.361-365
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• 1998

In this paper, the sliding mode variable structure-fuzzy(SMVS-F) control algorithm is applied to speed controller for field oriented induction motor drive system. According to the principle of sliding mode variable structure-fuzzy adjustable speed control scheme, the proposed algorithm shows good performances which are reducing chattering, robustness against parameter variation in induction motor drive. The validity of the proposed control scheme is verified by computer simulation using SIMULINK.

• 동력기계공학회지
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• 제3권2호
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• pp.70-78
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• 1999

The vector controlled induction motor(I.M) with speed sensor has been widely used for variable speed drive systems. In these application fileds, speed sensorless control are expected strongly to progress reliability, simplicity and cost performance of I.M and to expand its application part. This paper describes a novel speed sensorless control method of I.M based on feedforward quick torque response control technique. Especially, this paper aimed at the realization of sensorless control in the very low speed region, The proposed method can be formulated simply from a motor circuit equation and conducted easily by detecting primary motor currents and a voltage command at every sampling time. Throughout some results of numerical simulations with the assumption of using a pulse width modulation(PWM) voltage source inverter, the validity of the method was successfully confirmed.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.561-564
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• 2004

This paper proposes a new application of Kalman filter to estimate speed sensorless DC motor. Kalman filter can estimate the system state variables accurately; even the system input is disturbed with noise. In the design, the mathematical model of DC motor in discrete state-space form will be created; the speed of DC motor which is considered as state variable and can be estimated by using Kalman filter. In the experiment; TMS320C31 floating point digital signal processor is used for hardware implementation, the input is disturbed with/and without white noise in the experiment. The experimental results show the speed of DC motor which is estimated by Kalman filter has good accuracy when compared with the results from tacho-meter.