• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.12
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• pp.1253-1260
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• 1990

The traditional speed and pole position sensors, which are inevitable to drive the permanent magnet synchronous motors, are removed by the parameter identification using model reference adaptive systems. Also, the current detecting method is proposed on the synchronously rotating axis without the position information, and this enables the implementation of the proposed algorithm. The proposed methods have been confirmed both by digital simulation and experiments.

• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.310-313
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• 1988

It is an important thing to detect the Pole position for vector control of permanent magnet synchronous motor. In this paper, traditional position sensor is removed with the estimation of the pole position by using adaptive identification. Also, the proposed algorithm is simulated and the results are very good performance.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.518-521
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• 1999

This paper describes high speed squirrel cage induction motor drives without speed sensors using neural network based on Kalman filter Learning. High speed motors are receiving inverasing attentions in various applications, because of advantages of high speed, small size and light weight with same power level. Larning rate by Kalman filtering is time varying, convergence time fast, effect of initial weight between neurons is small.

• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.289-290
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• 2010

In this paper, a sturdy on the controller for super high speed PMSM is described. To drive PMSM at 60000rpm, the MRAS based sensorless control algorithm is applied. The control board was designed using TMS320F28335 and the performance verified by experiments.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2149-2151
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• 2002

In this paper, a novel speed estimation method of an induction motor using neural networks(NNs) is presented. The NN speed estimator is trained online by using the error backpropagation algorithm, and the training starts simultaneously with the induction motor working. The neural network based vector controller has the advantage of robustness against machine parameter variation. The simulation results using Matlab/Simulink verify the useful of the proposed method.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.11a
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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.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.3
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• pp.224-232
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• 2006

The paper proposes the artificial neural network(ANN) sensorless control of induction motor drive with adaptive fuzzy logic controller(AFLC). Also, this paper proposes the speed control of induction motor using AFC 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 AFLC and him controller. And this paper is proposed the results to verify the effectiveness of the AFLC and ANN controller.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.773-778
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• 2001

This paper describes the implementation of the vector control schemes for a variable-speed 131kW PMSM (Permanent Magnet Synchronous Motor) in super-high speed application. The vector control with synchronous reference frame current regulator has been implemented with the challenging requirements such as the extremely low stator inductance$(28^{\mu}H)$, the high dc link voltage(600V) and the high excitation frequency(1.2kHz). Because the conventional position sensor is not reliable in super-high speed, a vector control scheme without any position sensor has been proposed. The proposed sensorless algorithm is implemented by processing the output voltage of the PI current regulator, and hence the structure is simple and the estimated speed is robust to the measurement noise. The experimental system has been built and the proposed control has been implemented and evaluated. The test result, up to the speed of 60,000 r/min, shows the validity of the proposed control.

• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.2
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• pp.105-114
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• 2015

Heated glasses are widely used to prevent surface condensation and freezing in ship and building windows. This study proposes a heated glass temperature control system composed of power and control circuits to control the temperature of heated glasses. The proposed temperature control system adopts a digital controller instead of a conventional analog controller. Thus, the proposed system has better characteristics, such as precise setup and control of glass temperature, setup and control of output power, and control mode change between ON/OFF and phase controls. The system can also implement multi-functional control algorithms. The control characteristics are not dependent upon external disturbances, such as ambient temperature and electrical noises. Furthermore, the proposed temperature control system utilizes the power line communication (PLC) method to control the number of heated glasses without any extra communication lines. The system proposes a new communication protocol with strong immunity to electrical switching noises. A new sensorless algorithm is used to detect the temperature of the heated glass. This study presents the design guidelines in detail and its effectiveness are confirmed by implementing a 4-kw prototype temperature control system.

• Proceedings of the KIEE Conference
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• 2007.10c
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• pp.123-125
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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. This paper is proposed the analysis results to verify the effectiveness of the FLC-FNN and ANN controller.