• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.9
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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.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.585-587
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• 1998

It is suggested that the PWM inverter is controlled by Digital Software Programming. VVVF(Variable Voltage Variable Frequency) inverter control being used by PWM control for driving the motor with speed-varying, makes the PWM pattern with calculating the output voltage and frequency, and with controlling the carrier and signal, so actually this method is difficult to correspond with driving the motor by using voltage-varying and frequency-varying. Therefore this research suggested the new algorithm controlled by micro processor which is already stored by various PWM form of output voltage by using fundamental data of the carrier and signal. The PWM wave can be controlled with real time by using extra hardware and digital software and to speed up program processing, the control signals to switch the power semi-conductor of three phase PWM inverter, simultaneously use the output signal by microprocessor and extra hardware, and control signal by software. In the end, this method was proved by applying to Three Phase Voltage-type Inverter.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.12
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• pp.691-696
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for induction motor speed control. 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. Proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using dSPACE(DS1102) board are performed to verify the effectiveness of the proposed scheme.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.181-185
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• 2000

To get high efficiency in variable speed control of induction motor it is required that the vector control should be separated from flux components current and torque component current. In this paper the vector control is modeled by the estimation of the stator flex. Representing induction motor speed controller as a digital system with he use of he 32bit DSP improves the motor control performance The IGBT is used as the switching device and the validity of the proposed vector control is proved through voltage current wave and the characteristics of the velocity response as the drive circuit being simplified

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.6-11
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• 1988

Alight-weight robot arm carrying a payload is modelled as a cantilever beam with a tip mass subjected to a high speed rotation. Equations of Motion, for modal control, are represented as discrete state variable form. Digital optimal control law with observer is developed to suppress the arm vibration and control the position of the joint angle. The effects of the number of controlled modes, weighting factors of the performance index, reference rotation time, and sampling time on the control performance are analyzed by computer simulation and experiments.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.11
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• pp.753-759
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• 2004

This paper presents a modeling and simulation of a fuzzy controller for maximum power extraction of a grid-connected wind energy conversion system with a link of a rectifier and an inverter. It discusses the maximum power control algorithm for a wind turbine and proposes, in a graphical form, the relationships of wind turbine output, rotor speed, power coefficient, tip-speed ratio with wind speed when the wind turbine is operated under the maximum power control. The control objective is to always extract maximum power from wind and transfer the power to the utility by controlling both the pitch angle of the wind turbine blades and the inverter firing angle. Pitch control method is mechanically complicated, but the control performance is better than that of the stall regulation method. The simulation results performed on MATLAB will show the variation of generator's rotor angle and rotor speed, pitch angle, and generator output.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.54 no.10
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• pp.497-504
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• 2005

For maximum output power, wind turbines are usually controlled at the speed which is determined by the optimal tip-speed ratio. This method requires information of wind speed and the power conversion coefficient which is varied by the pitch angle control. In this paper, a new maximum output power control algorithm using fuzzy logic control is proposed, which doesn't need this information. Instead, fuzzy controllers use information of the generator speed and the output power. By fuzzy rules, the fuzzy controller produces a new generator reference speed which gives the maximum output power of the generator for variable wind speeds. The proposed algorithm has been implemented for the 3[kW] cage-type induction generator system at laboratory, of which results verified the effectiveness of the algorithm.

• The Transactions of the Korean Institute of Power Electronics
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• v.2 no.1
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• pp.39-45
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• 1997

In this paper, a high speed motor drive system using an indirect adaptive neural network controller is proposed. In the variable high speed motor drives, the speed response can be deteriorated by long settling time and high overshoot. To obtain a good dynamical performance, an adaptive feedforward controller consisted of Neural Network Controller(NNC) and Neural Network Emulator(NNE) is applied. The NNE is used to identify the parameters and characteristics of high speed motor. To train the controller, the weights are dynamically adjusted using the back propagation algorithm. Computer simulation and implementation of the proposed system is described.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5416-5426
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• 2012

This study uses the algorithm which estimates the magnetic flux using different models in the low speed driving area and the high speed driving area by the voltage-current model synthesis magnetic flux Estimator and, from this result, estimates the magnetic flux angle to achieve the stable speed control through all the areas from the low speed to the high speed drive. In particular, the current change and the magnetic flux change under variable load were estimated in real time in the low speed area and this made the control characteristic improved in the low speed area. According to this, even under variable load, the more stable simulation and experiment could have been completed using PI current controller and PI flux controller in all the areas. As a result, the outstanding speed control characteristic has been achieved.

• 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.