• 한국태양에너지학회:학술대회논문집
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• 2008.04a
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• pp.61-67
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• 2008

In this paper proposed the solar tracking system to use a fuzzy based on PC in order to increase an output of the PV array. The solar tracking system operated two DC motors driving by signal of photo sensor. The control of dual axes is not an easy task due to nonlinear dynamics and unavailability of the parameters. Recently, artificial intelligent control of the fuzzy control, neural-network and genetic algorithm etc. have been studied. The fuzzy control made a nonlinear dynamics to well perform and had a robust and highly efficient characteristic about a parameter variable as well as a nonlinear characteristic. Hence the fuzzy control was used to perform the tracking system after comparing with error values of setting-up. nonlinear altitude and azimuth. In this paper designed a fuzzy controller for improving output of PV array and evaluated comparison with efficient of conventional PI controller. The data which were obtained by experiment were able to show a validity of the proposed controller.

• 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 the Korean Institute of Intelligent Systems
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• v.4 no.1
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• pp.35-49
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• 1994

It is well known that, for nominimum phase systems, a conventional linear controller of PID type or an adaptive controller of this structure shows limitation in achieving a satisfactory performance under tight specifications. In this paper, we combine a neuro-controller with a PI-controller with off-line learning capability provided by the Genetic Algorithm to propose a novel neuro-controller to control nonminimum phase systems effectively. The simulation results show that our proposed model is more efficient with faster rising time and less undershoot effect when the performances of the proposed controller and a conventional form are compared.

• Proceedings of the KIPE Conference
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• 2008.10a
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• pp.199-201
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• 2008

In this paper proposed the solar tracking system to use direct fuzzy control order to increase an output of the PV (Photovoltaic) array. The solar tracking system operated two DC motors driving by signal of photo sensor. The control of dual axes is not an easy task due to nonlinear dynamics and unavailability of the parameters. Recently, artificial intelligent control of the fuzzy control, neural-network and genetic algorithm etc. have been studied. The fuzzy control made a nonlinear dynamics to well perform and had a robust and highly efficient characteristic about a parameter variable as well as a nonlinear characteristic. Hence the fuzzy control was used to perform the tracking system after comparing with error values of setting-up, nonlinear altitude and azimuth. In this paper designed a DFC(Direct Fuzzy Control)controller for improving output of PV array and evaluated comparison with efficient of conventional PI controller. The data which were obtained by experiment were able to show a validity of the proposed controller.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1229-1236
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• 2022

In this paper, we studied accident-aware smart streetlights to prevent secondary accidents when driving on highways. It used Arduino and sensors to inform drivers of weather conditions, incorporated functions such as LED brightness control according to sunlight and night driving vehicles, and used Raspberry Pi camera OpenCV to learn various traffic accidents, natural disasters, and wildlife.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.7
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• pp.15-25
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• 2011

This proposes an online loss minimization algorithm for series PI(SPI) based interior permanent magnet synchronous motor(IPMSM) drive to yield high efficiency and high dynamic performance over wide speed range. The loss minimization algorithm is developed based on the motor model. In order to minimize the controllable electrical losses of the motor and thereby maximize the operating efficiency, the d-axis armature current is controlled optimally according to the operating speed and load conditions. For vector control purpose, a SPI is used as a speed controller which enables the utilization of the reluctance torque to achieve high dynamic performance as well as to operate the motor over a wide speed range. Also, this paper proposes current control of model reference adaptive fuzzy controller(MFC), and estimation of speed using artificial neural network(ANN) controller. The proposed efficiency optimization control, SPI, MFC, ANN in this paper is applied to IPMSM drive system, the validity of this paper is proved by analyzing response characteristics in variety operating conditions.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.5
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• pp.16-28
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• 2010

This paper proposes maximum torque control of SynRM drive using artificial intelligent(AI)PI 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 axis current for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled AIPI and ANN controller and the operating characteristics controlled by maximum torque control are examined in detail.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.10a
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• pp.315-318
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• 2008

In this paper proposed the solar tracking system to use a fuzzy control order to increase an output of the PV(Photovoltaic) array. The solar tracking system operated two DC motors driving by signal of photo sensor. The control of dual axes is not an easy task due to nonlinear dynamics and unavailability of the parameters. Recently, artificial intelligent control of the fuzzy control, neural-network and genetic algorithm etc. have been studied. The fuzzy control made a nonlinear dynamics to well perform and had a robust and highly efficient characteristic about a parameter variable as well as a nonlinear characteristic. Hence the fuzzy control was used to perform the tracking system after comparing with error values of setting-up, nonlinear altitude and azimuth. In this paper designed a fuzzy controller for improving output of PV array and evaluated comparison with efficient of conventional PI controller. The data which were obtained by experiment were able to show a validity of the proposed controller.

• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.32-34
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• 2009

The field oriented control of induction motors is widely used in high performance applications. However, detuning caused by parameter disturbance still limits the performance of these drives. In order to accomplish variable speed operation, conventional PI-like controllers are commonly used. These controllers provide limited good Performance over a wide range of operation, even under ideal field oriented conditions. This paper is proposed model reference adaptive fuzzy control(MFC) and artificial neural network(ANN) based on the vector controlled induction motor drive system. Also, this paper is proposed control of speed and current using fuzzy adaption mechanism(FAM), MFC and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM, MFC and ANN controller. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.404-407
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• 2009

The field oriented control of induction motors is widely used in high performance applications. However, detuning caused by parameter disturbance still limits the performance of these drives. In order to accomplish variable speed operation conventional PI-like controllers are commonly used. These controllers provide limited good performance over a wide range of operation even under ideal field oriented conditions. This paper is proposed adaptive fuzzy controller(AFC) and artificial neural network(ANN) based on the vector controlled induction motor drive system. Also, this paper is proposed control of speed and current using fuzzy adaptation mechanism(FAM), AFC and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM, AFC and ANN controller. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.