• The Transactions of the Korean Institute of Electrical Engineers P
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• v.55 no.3
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• pp.133-140
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• 2006

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor(SynRM) which minimizes the copper and iron losses. Also, this paper presents a speed estimated control scheme of SynRM using artificial neural network(ANN). There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization controller is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. It is shown that the current components which directly govern the torque production have been very well regulated by the efficiency optimization control scheme. The proposed algorithm allows the electromagnetic losses in variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of ANN is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.215-219
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• 2005

In this paper, a novel motor control method is proposed to improve the performance of sensorless drive of BLDC motors. The unknown input (back-EMF) is modelled as the additional state of system in this paper. Taking into account the disturbance adopted by the back-EMF, the observer can be obtained by the augmented system equation. An algorithm to detect the back-EMF of the BLDC motor using the state observer is constructed. As a result, the novel sensorless drive of BLDC motors that can strictly estimate rotor position and speed is proposed.

• Proceedings of the KIEE Conference
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• 2005.10c
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• pp.175-177
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• 2005

An interior permanent magnet synchronous motor (IPMSM) is receiving increased attention for many industrial applications because of its high torque to inertia ratio, superior power density, and high efficiency. This paper presents algorithm for speed sensorless vector control based on a fuzzy controller and an instantaneous reactive power. Effectiveness of algorithm is confirmed by the experiments.

• Proceedings of the KIEE Conference
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• 1997.07f
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• pp.2129-2131
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• 1997

A sensorless controller of induction motor has several advantage: availability in a harsh environment. In this paper, the speed information is driving from the currents and the estimated stator flux. To obtain the estimated stator flux, this study is using The Estimator. The simulation results show that the proposed scheme has activity over a wide speed range and good response to load variations

• 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.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.48 no.11
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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.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.526-530
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• 1999

This paper presents a newly developed speed sensorless drive using Neural Network algorithm. Neural Network algorithm can be divided into three categories. In the first one, a Back Propagation-based NN algorithm is well-known to gradient descent method. In the second scheme, a Extended Kalman Filter-based NN algorithm has just the time varying learning rate. In the last scheme, a Recursive Least Square-based NN algorithm is faster and more stable than the classical back-propagation algorithm for training multilayer perceptrons. The number of iterations required to converge and the mean-squared error between the desired and actual outputs is compared with respect to each method. The theoretical analysis and experimental results are discussed.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.27-29
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• 2005

This paper studies particularly applicable method for sensorless Permanent Magnet Brushless DC(PMBLDC) Motor drive system. The waveform of the motor internal voltages (or back emf) contains a fundamental and higher order frequency harmonics. Therefore the third harmonics. component is extracted from the stator phase voltage. The resulting third harmonic signal keeps a constant phase relationship with the rotor flux for any motor speed and load condition. In addition, a simple starting method and a speed estimation approach are also proposed.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.230-230
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• 2000

This paper describes a model reference adaptive system(MRAS) for speed control of vector-controlled induction motor without a speed sensor. The proposed approach is based on observing the instantaneous torque. The real torque is calculated by sensing stator current and estimated torque is calculated by stator current that is calculated by using estimated rotor speed. The speed estimation error is linearly proportional to error between real torque and estimated torque. The proposed feedback loop has linear component. Furthermore proposed method is robust to parameters variation. The effectiveness is verified by equation and simulation

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.234-234
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• 2000

A direct torque control(DTC) based sensorless speed control system which employs a new closed loop flux observer is proposed. The flux observer takes an adaptive scheduling gains where motet speed is used as the scheduling variable. Adaptive nature comes from the fact that the estimated values of stator resistance and speed are included as observer parameters. The parameters of the PI controllers adopted in the adaptive law for the estimation of stator resistance and motor speed are determined by simple genetic algorithm. Simulation results in low speed region are given for comparison between proposed and conventional flux estimate scheme.