• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1086-1087
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• 2007

This paper presents a implementation of speed sensorless vector control algorithm of induction motor using MATLAB/SIMULINK. The proposed method utilize the combination of the voltage model based on stator equivalent model and the current model based on rotor equivalent model, which enables stable estimation of rotor flux. Estimated rotor speed, which is used to speed controller of induction motor, is based on estimated flux. The overall system consisted of speed controller with the most general PI controller, current controller, flux controller. Speed sensorless vector control algorithm is implemeted as block diagrams using MATLAB/SIMULINK. Realtime control is perform by dSPACE DS1104 control board and Real-Time-Interface(RTI).

• The Transactions of the Korean Institute of Power Electronics
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• v.17 no.3
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• pp.238-245
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• 2012

This paper presents a sensorless speed control of a 2-phase switch reluctance motor(SRM). The proposed sensorless control scheme is based on the slide mode observer with parameter compensator to improve the estimation performance. In the stand still position, the initial rotor position is determined by pulse current responses of each phase windings and the current difference. In order to determine an accurate initial rotor position, the two initial rotor positions are estimated by the difference of the pulse currents. From the stand still to the operating region, a simple open loop control which determines the commutation sequence by the pulse current of the unexcited phase winding is used. When the motor speed is reached to the sensorless control region, the estimated rotor position and speed by the slide mode observer are used to control the SRM. The flux calculator used in the slide mode observer is designed by phase voltage and the voltage drops in the phase resistance of the winding. The accuracy of the flux calculator is dependent on the phase resistance. For the continuous update of the phase resistance, current gradient at the inductance break point is used in this paper. The error of the estimated rotor position at the current gradient position is used to update the phase resistance to improve the sensorless scheme. The proposed sensorless speed control scheme is verified with a practical compressor used in home appliances. And the results show the effectiveness of the proposed control scheme.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.2
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• pp.339-346
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• 2017

This paper proposes a sensorless control method to improve the performance of an internal permanent magnet synchronous motor (IPMSM) control by using a full-order flux observer in a wide speed range. The conventional sensorless control method uses a constant gain for high performance at low-speed region. However, this method has drawbacks such as an increased angle error and current ripple in the high-speed region due to the fixed gain value. In order to overcome this problem, the gain of the full-order flux observer is changed by considering the angle error in the whole speed range. The proposed method minimizes the angle error for each region of the speed range by applying a relevant gain value, which improves the current ripple reduction and motor noise cancellation. The validity of proposed sensorless control method is verified by a simulation and an experiment.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.248-252
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• 2003

This paper has a control method proposed for the effective self-tuning fuzzy speed control based on neural network of the induction motor indirect vector control. The vector control of an induction motor provides the decoupled control of the rotor flux magnitude and the torque producing current to performance is desirable. But, the drive performance often degrades for the machine parameter variations and its condition give rise to coupling of flux and torque current. The fuzzy speed control of an induction motor has the robustness about machine parameter variations compared with conventional PID speed control in a way. That proved to be some waf from the true. The purpose of this paper is to improve the adaptation by offering self-turning function to fuzzy speed controller. In this paper, the adaptive mechanism of fuzzy speed control in used ANN(Artificial Neural Network) technique is applied in an IFO induction machine drive, such that the machine can follow a reference model (an ideal field oriented machine) to achieve desired speed. In this paper proved the self-turning method of fuzzy controller has the robustness about parameter variation and the wide range of adaptation by simulation.

• 연구논문집
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• s.27
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• pp.141-151
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• 1997

Recently, the importance of electro-multifunction control valve which is digital control valve is increasing day after day. This is due to the demand of that the current industrial world wants the valve having simpler control circuit and higher operation reliability. It is the PWM controlled high speed electronic valve that satisfies the damands of current industrial world. But, the PWM controlled high speed electronic valve has some non-linearity characteristics like as the delay time of switching and the pressure oscillation phenomenon. These characteristics are an obstacle for the control of high speed & high efficiency in control system. Therefore, in this study, we set the studying purpose on analysis of parameter characteristics in solenoid, and on establishment of optimum design technique in high speed solenoid.

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.1003-1005
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• 2000

The switched reluctance drive provides a good adjustable steed characteristics like a D.C series motor. However, because of the torque production mechanism, it also has some disadvantage such as higher torque ripple and fluctuation in speed. To reduce torque ripple and to control speed precisely at low condition current level technique is adopted. The SRM drive with current control of invariable frequency and phase swichting control has good speed regulation characteristics. Digital control system with an 80c196kc micro-controller is used to realize this drive system. Test results show that the control system has the goof ability to achive precise speed control at low speed.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.416-419
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under-parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of loaming through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive loaming mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control(FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• Journal of Power Electronics
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• v.7 no.1
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• pp.81-88
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• 2007

This paper presents a new approach to the automatic control of the turn-off angle used to excite the Switched Reluctance Motor (SRM) employed in electric vehicles (EV). The controller selects the turn-off angle that supports and improves the performance of the motor drive system. This control scheme consisting of classical current control and speed control depends on a lookup table to take the best result of the motor. The turn-on angle of the main switches of the inverter is fixed at $0^{\circ}C$ and the turn-off angle is variable depending on the reference speed. The motor, inverter and control system are modeled in Simulink to demonstrate the operation of the system.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.3
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• pp.249-256
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive learning mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control (FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN, FLC and ANN controller.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.115-118
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• 2002

In this paper, a current control method using adjustment of the phase advance angle is proposed. This proposed method improves the torque and speed response characteristic by minizing delay of current at high speed operation, and also make reverse motoring possible. Simulated results prove the effectiveness of the propposed method through comparison with the conventional unipolar PWM method.