• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1995.10a
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• pp.96-100
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• 1995

In this paper an induction motor control using fuzzy controller and neural network adptive observer is studied. The proposed observer which comprises neural network flux observer which comprises neural network flux observer and neural network torque observer is trained to learn the flux dynamics and torque dynamics and subjected to further on-line training by means of a backpropagation algorithem. Therefore it has been shown that the robust control of induction motor neglects the rotor time constant variations

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1646_1647
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• 2009

In this paper, Takagi-Sugeno(T-S) fuzzy controller and search method are developed for efficiency optimization of induction motors(IMs). The proposed control scheme consists of efficiency controller and adaptive backstepping controller. A search controller for which information of input of T-S fuzzy controller is included in efficiency controller that uses a direct vector controlled induction motor. A sliding mode observer is designed to estimate rotor flux and an adaptive backstepping controller is used to control of speed of IMs. Simulation results are presented to validate the proposed controller.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1505-1506
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• 2008

In this paper, a robust adaptive backstepping control is developed for efficiency optimization of induction motors with uncertainties. The proposed control scheme consists of efficiency flux control(EFC) using a sliding mode adaptive flux observer and robust speed control(RSC) using a function approximation for mechanical uncertainties. In EFC, it is important to find the flux reference to minimize power losses of induction motors. Therefore, we proposed the optimal flux reference using the electrical power loss function. The sliding mode flux observer is designed to estimate rotor fluxes and variation of inverse rotor time constant. In RSC, the unknown function approximation technique employs nonlinear disturbance observer(NDO) using fuzzy neural networks(FNNs). The proposed controller guarantees both speed tracking and flux tracking. Simulation results are presented to illustrate the effectiveness of the approaches proposed.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.52 no.11
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• pp.535-540
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• 2003

Since axial flux motor has an advantage over more conventional radial flux type motor such as high power density, it can be used as a power train for hybrid electric vehicle and electric vehicle. Also operating range can be extended and efficiency can be improved by changing air gap. Optimal operating air gap is estimated based on the measured efficiency at different air gap. Motor model is developed based on estimated optimal air gap and efficiency. Motor/controller performance is analyzed through simulation. Possible application area of axial flux motor was explored through simulation.

• Proceedings of the KIPE Conference
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• 2007.07a
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• pp.391-393
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• 2007

The rotor speed and flux information is most important in the vector control. The accuracy of flux observers for induction machine inherently depends on parameter sensitivity. The control strategy is using online flux observer for flux estimation. In the proposed system, the speed control characteristics using a online flux observer control isn't affected by a load torque parameter disturbance. Simulation results are presented to prove the effectiveness of the adaptive sliding mode controller for the drive variable load of induction motor.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.311-313
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• 1996

In case of field weakening region, the dynamic behavior of the speed controller depends on the rotor flux level. In this region, the flux is decreased inversely proportional to the rotor speed. As the rotor flux is decreased, as the magnetizing inductance is increased. In this paper, the effect of this increased magnetizing inductance to the performance of vector control is illustrated. The stationary reference frame torque not including the magnetizing inductance is calculated by stationary stator flux, and the rotating reference frame torque including the magnetizing inductance is calculated by rotating rotor flux. If the magnetizing inductance value is constant, two torque values are same regardless of the flux-component current. However, if the magnetizing inductance is varied, those two values are different. The paper presents the new tuning scheme of the magnetizing inductance using the difference between the stationary and rotating torque. Computer simulation demonstrates the efficacy of the proposed scheme.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.451-459
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• 2018

This work presents an approach for modeling of electric vehicle considering the vehicle dynamics, drive train, rotational wheel and load dynamics. The system is composed of IPMSM (Interior Permanent Magnet Synchronous Motor) coupled with the wheels through a drive train. Generally, IPMSM is controlled by ordinary PID controllers. Performance of the ordinary PID controller is not satisfactory owing to the difficulties of optimal gain selections. To overcome this problem, a new type of fuzzy logic gain tuner for PID controllers of IPMSM is required. Therefore, in this paper fuzzy logic based gain tuning method for PID controller is proposed and compared with some previous control techniques for the better performance of electric vehicle with an optimal balance of acceleration, speed, travelling range, improved controller quality and response. The model was developed in MATLAB/Simulink, simulations were carried out and results were observed. The simulation results have proved that the proposed control system works well to remove the transient oscillations and assure better system response in all conditions.

• Journal of the Korean Society of Safety
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• v.14 no.4
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• pp.108-113
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• 1999

This paper presents a study on fuzzy speed and flux controller used in a vector control of a CRPWM(Current Ragulated PWM) induction motor drive. In this paper, an approach for an easier design of the fuzzy controller is presented in order to obtain the desired value for the response time with minimal overshoot and to improve the steady state performance for speed step commands. The fuzzy controller is constructed only upon the knowledge of the motor behaviour and the desired speed response, and provides fast and robust control by reducing the effects of nonlinearities, parameter changes and load disturbance. The results of applying the fuzzy logic controller to an IM drive system are compared with those obtained by application of a conventional PI controller. The fuzzy controller provided a better response than the PI controller.

• Proceedings of the KIPE Conference
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• 2009.11a
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• pp.252-255
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• 2009

This paper proposes a new loss minimization controller (LMC) for induction motor drive. The proposed LMC presents a strategy to minimize the total power losses of induction motor (IM), which is based on simplified equivalent circuit and simplified model of IM. The proposed controller using the field oriented control (FOC) method is to determine an optimal rotor flux for obtaining the minimum total power losses and higher efficiency. Simulation and experimental results are given to validate the effectiveness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.314-317
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• 1996

In this paper, we present a nonlinear adaptive controller for position tracking of induction motors. In constructing the adaptive controller, a backstepping approach is used under the condition of full state information, while a nonlinear observer is adopted for rotor flux estimation. The adaptive controller is shown to drive the state variables of system to the desired ones asymptotically and whose effectiveness is also shown via computer simulation.