• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.348-354
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• 1999

In recent years, a PID controller has been used as a major control method in real control processes. This controller requires a determination of PID control gains. But it is difficult to select the best gains theoretically. Thus there have been many approaches to determine them empirically Most of them are based on experience and knowledge. In this paper, we proposed a tuning method of the PID Parameters by using neural network. To show effectiveness of the proposed method, the simulation of DC motor and one link manipulator position control is carried out.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.11 s.353
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• pp.98-104
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• 2006

The conventional burst-mode APC(Automatic Power Control) circuit had an effective structure that was suitable for a low power consumption and a monolithic chip. However, as data rate was increased, it caused errors due to the effect of the zero density. In this paper, we invented a new structured peak-comparator which could compensate the unbalance of the injected currents using double gated MOS and MOS diode. And we proposed a new burst-mode APC adopting it. The new peak-comparator in the proposed APC was very robust to zero density variations maintaining the correct decision point of the current comparison at high data rate. It was also suitable for a low power consumption and a monolithic chip due to lack of large capacitors.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.4
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• pp.67-74
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• 2004

As data rate was increased, the conventional burst-mode automatic power control circuit caused errors due to the effort of the mark density variation. To solve this problem we invented a new structured peak-comparator which could eliminate the effect of the mark density variation even in high date rate, and revised the conventional one using it. We proposed a burst-mode automatic power control circuit robust to mark density variations. We found that the peak-comparator in the proposed automatic power control circuit was very robust to mark density variations because it affected very little by the mark density variation in high date rate and in the wide variation range of the reference current and the difference current.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.6
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• pp.1117-1122
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• 2009

In this work, temperature sensor and control circuit for measuring temperature are proposed. The proposed circuit can be fabricated without additional CMOS fabrication process and the output of proposed circuit is digital value. The supply voltage is 5volts and the circuit is designed by using 0.5${\mu}m$ CMOS process. The circuit for measuring temperature consists of PWM control circuit, VCO, counter and register. consisted The frequency of PWM control circuit is 23kHz and the frequency of VCO is 416kHz, 1MHz and 2MHz, respectively. The circuit operation is analyzed by using SPICE.

• 전기산업
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• v.1 no.2
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• pp.40-51
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• 1990

• ICROS
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• v.1 no.3
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• pp.22-38
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• 1995

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.6
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• pp.509-516
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• 2002

This paper presents a simple torque estimation method and switching angle control of Switched Reluctance Motor(SRM) using Neural Network(NN). SRM has gaining much interest as industrial applications due to the simple structure and high efficiency. Adaptive switching angle control is essential for the optimal driving of SRM because of the driving characteristic varies with the load and speed. The proper switching angle which can increase the efficiency was investigated in this paper. NN was adapted to regulate the switching angle and nonlinear inductance modelling. Experimental result shows the validity of the switching angle controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.7
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• pp.103-108
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• 2007

Neural network is used in many fields of control systems currently. However, It is not easy to obtain input-output pattern when neural network is used for the system of a single feedback controller and it is difficult to get satisfied performance with neural network when load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object in place of activation function of Neural Network output node. As the Neural Network self adaptive control system is designed in simple structure neural network input-output pattern problem is solved naturally and real tin Loaming becomes possible through general back propagation algorithm. The effect of the proposed Neural Network self adaptive control algorithm was verified in a test of controlling the speed of a A.C. servo motor equipped with a high speed computing capable DSP (TMS320C32) on which the proposed algorithm was loaded.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.7
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• pp.61-67
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• 2002

In this study a control surface/actuator fault detection, identification, and accommodation system for aircraft is designed. This fault tolerant control system tries to return aircraft to its stable trim condition in a short time. The control system is designed using neural networks with Extended Back Propagation Algorithm which shows fast convergence. F-4 aircraft with possible stabilator or aileron failure/stuck is simulated with the proposed scheme.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1587-1588
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• 2007

IPM(Interior Permanent Magnet) Machine의 전류 제어는 자기저항에 의존하는 토크특성 때문에 SPM(Surface Permanent Ma- gnet) Machine보다 복잡하다. 고성능 토크제어를 위해서는 d축 전류와 q축 전류의 동특성간의 상태 decoupling이 요구된다. 그러나 전류의 상태 동특성이 coupling된 인덕턴스의 변화(온도, 파라미터들의 부정확한 측정값)는 상태 decoupling을 어렵게 한다. 그래서 이러한 변화와 각각의 전류가 독립적으로 제어될 수 있게 여러 decoupling 방법들에 초점이 맞춰지고있다. 본 연구는 외란에 강하고, 특히 인덕턴스의 변화와 상관없는 이상적인 토크제어를 하기 위해 신경회로망을 이용하여 슬라이딩 평면(sliding surface)을 구성하고, SMC(Sliding Mode Control)를 이용하여 상태 cross-coupling의 decoupling을 위한 새로운 접근을 제안한다. 이 방법은 PI제어 성능과 SMC의 강인성을 알고리즘을 이용하여 결합한 것이라고 볼 수도 있다.