• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.29-31
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• 2009

Optimal efficiency control of synchronous reluctance motor(SynRM) is very important in the sense of energy saving and conservation of natural environment because the efficiency of the SynRM is generally lower than that of other types of AC motors. This paper is proposed an efficiency optimization control for the SynRM which minimizes the copper and iron losses. The design of the speed controller based on fuzzy-neural networks (FN)-PI controller that is implemented using fuzzy control and neural networks. There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization control 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 the proposed controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• Journal of Power Electronics
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• v.8 no.3
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• pp.280-290
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• 2008

This paper deals with an investigation and evaluation of the performance of a state observer based Permanent Magnet Synchronous Motor (PMSM) drive controlled by PI (Proportional Integral), PID (Proportional Integral and Derivative), SMC (sliding mode control), ANN (Artificial neural network) and FLC (Fuzzy logic) speed controllers. A detailed study of the steady state and dynamic performance of estimated speed and angle is given to demonstrate the capability of the controllers.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.77-78
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• 2008

This paper presents to control speed of induction motors with uncertainties. We use an adaptive backstepping controller with fuzzy neural networks(FNNs) and model reference adaptive system(MRAS) at Indirect vector control method. The adaptive backstepping controller using FNNs can control speed of induction motors even we have a minimum of information. And this controller can be used to approximate most of uncertainties which are derived from unknown motor parameters, load torque such as disturbances. MRAS estimates to rotor resistance and also can find optimal flux to minimize power losses of Induction motor. Indirect vector PI current controller is used to keep rotor flux constant without measuring or estimating the rotor flux. Simulation and experiment results are verified the effectiveness of this proposed approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2389-2413
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• 2015

High data rates in long-term evolution (LTE) networks can affect the mobility of networks and their performance. The speed and motion of user equipment (UE) can compromise seamless connectivity. However, a proper handover (HO) decision can maintain quality of service (QoS) and increase system throughput. While this may lead to an increase in complexity and operational costs, self-optimization can enhance network performance by improving resource utilization and user experience and by reducing operational and capital expenditure. In this study, we propose the self-optimization of HO parameters based on fuzzy logic control (FLC) and multiple preparation (MP), which we name FuzAMP. Fuzzy logic control can be used to control self-optimized HO parameters, such as the HO margin and time-to-trigger (TTT) based on multiple criteria, viz HO ping pong (HOPP), HO failure (HOF) and UE speeds. A MP approach is adopted to overcome the hard HO (HHO) drawbacks, such as the large delay and unreliable procedures caused by the break-before-make process. The results of this study show that the proposed method significantly reduces HOF, HOPP, and packet loss ratio (PLR) at various UE speeds compared to the HHO and the enhanced weighted performance HO parameter optimization (EWPHPO) algorithms.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1727-1730
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• 2003

A windshield wiper system plays a key part in assurance of driver's safety at rainfall. However, because quantity of rain and snow vary irregularly according to time and velocity of automotive, a driver changes speed and operation period of a wiper from time to time in order to secure enough visual field in the traditional windshield wiper system. Because a manual operation of windshield wiper distracts driver's sensitivity and causes inadvertent driving, this is becoming direct cause of traffic accident. Therefore, this paper presents the basic architecture of vision-based smart windshield wiper system and the rain sensing algorithm that regulate speed and operation period of windshield wiper automatically according to quantity of rain or snow. Also, this paper introduces the fuzzy wiper control algorithm based on human's expertise, and evaluates performance of suggested algorithm in simulator model. In especial, the vision sensor can measure wide area relatively than the optical rain sensor. hence, this grasp rainfall state more exactly in case disturbance occurs.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1167-1169
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• 1996

As electrical discharge machinery(EDM) is industrial process which is manufactured by discharge energy, by producing discharge, EDM process finished material at the little micrometer air gap. Especially, EDM is used for the characteristic of heat-resisting material, it puts to use air-space industrial element, confusing shaping material such as jet engine, rocket elements. Working performance. is changed by environment of working, discharge current voltage and duration of discharge pulse. Evaluation of performance working is work speed, clearance smoothing of product surface, wasting of pole. In this paper, this machine is compensated by adaptive controller which corrects the weak points of classical machine which is observed and operated working condition by user in hands. The previous purpose is main object in this thesis. The adaptive controller automatically detect abnormal condition and working conditions. To improve performance, surface smoothing and working speed, the adaptive controller uses fuzzy control strategy. To evaluate performance, this controller is implemented by microprocessor i80c196 and is applied real experimental machine.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.10
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• pp.47-57
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• 2006

The paper is proposed maximum torque control of SynRM drive using adaptive teaming mechanism-fuzzy neural network(ALM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $^i{_d}$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.10-13
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• 2002

In this study, we used a 16-bit microprocessor, 80C196KC for a control part in order to develop a multi-functional wheel-chair system, and implemented a joy-stick to control this system. For the complete system, we used a commercial electromotive wheelchair as a basic plant, and applied an encoder to get the rotating number of the motor to transfer data to the MCU to control the motor. We used PWM (Pulse Width Modulation) method to control the wheel-chair motor where a H-bridge circuit was configured. We used the fuzzy control algorithm for the operation of DC motor, which was attached to the electromotive wheelchair and manipulated following the change of the joystick position while a user was controlling the Joystick. He also could control the speed and direction of DC motor as well as control position information.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.776_777
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• 2009

Optimal efficiency control of synchronous reluctance motor(SynRM) is very important in the sense of energy saving and conservation of natural environment because the efficiency of the SynRM is generally lower than that of other types of AC motors. This paper is proposed a novel efficiency optimization control of SynRM considering iron loss using neural network(NN). The optimal current ratio between torque current and exciting current is analytically derived to drive SynRM at maximum efficiency. This paper is proposed an efficiency optimization control for the SynRM which minimizes the copper and iron losses. The design of the speed controller based on adaptive learning mechanism fuzzy-neural networks(ALM-FNN) controller that is implemented using fuzzy control and neural networks. The objective of the efficiency optimization control is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. The control performance of the proposed controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.353-356
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• 1997

Vehicle steering system determines the direction of a vehicle. A manual steering system consists of mechanical connections between the steering wheel and tires. Recent power steering system adds an actuator to help a driver to steer easily at low speed. However, at front collision, the driver can be injured by steering shaft and the power steering pump decreases the engine power. To solve these problems, electronic power steering system which connects the steering wheel and tires with electronic connection is proposed, that has advantages such as decrease of engine load and increase of driver safety reactive. Since the ratio between driver's steering torque and steering torque of tires can be controlled freely, the torque which is delivered from the road to the driver through tires and steering wheel can be reshaped to make the driver feel comfortable. In this paper, the ratio of delivering steering torque and the magnitude of force to be delivered from road to driver has been controlled using fuzzy controller, and it's effectiveness has been shown through simulation results.