• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2002.11a
• /
• pp.113-119
• /
• 2002

Recently, an automatic crane control system is required with high speed and rapid transportation. During the operation of crane system in container yard it is necessary to control the crane trolley position and loop length so that the swing of the hanging container is minimized We can do development of unmanned automation control system using automation travel control technique and anti-sway technique in crane system. Therefore, we designed a controller for Automation travel control to control the transfer crane system. Analyzed crane system through simulation, and proved excellency of control performance than other conventional controllers.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.1
• /
• pp.8-14
• /
• 2013

In this paper, a control system for two wheeled inverted pendulum robot is implemented to have more stable balancing capability than the conventional control system. Fuzzy control structure is chosen for the two wheeled inverted pendulum robot, and fuzzy membership function factors for the control system are obtained for 3 specified weights using a trial-and-error method. Next a neural network is employed to generate fuzzy membership function factors for more stable control performance when the weight is arbitrarily selected. Through some experiments, we find that the proposed fuzzy control system using the neural network is superior to the conventional fuzzy control system.

• Journal of Korean Electronics
• /
• v.5 no.4
• /
• pp.99-102
• /
• 1985

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.9
• /
• pp.1871-1880
• /
• 2011

In this paper, a controller for two wheeled inverted pendulum robot, i.e., Segway type robot that is a convenient and easily handled vehicle is designed to have more stable balancing and faster velocity control compared to the conventional method. First, a widely used PID control structure is applied to the two wheeled inverted pendulum robot and proper PID control gains for some specified weights of users are obtained to get accurate balancing and velocity control by use of experimental trial-and-error method. Next, neural network is employed to generate appropriate PID control gains for arbitrarily selected weight. Here the PID gains based on the trial-and-error method are used as training data. Simulation study has been carried out to find that the performance of the designed controller using the neural network is more excellent than the conventional PID controller in terms of faster balancing and velocity control.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.20 no.7
• /
• pp.65-73
• /
• 2006

This paper presents speed control of IPMSM drive using neural network(NN) PI controller. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, NNPI controller proposes a new method based neural network. NNPI controller is developed to minimize overshoot rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Proceedings of the Optical Society of Korea Conference
• /
• 2002.07a
• /
• pp.32-33
• /
• 2002

적응광학계(adaptive optics system ; AO)는 파면을 파면측정장치로 측정하고 제어용 컴퓨터를 사용하여 파면보정장치를 구동함으로써 파면의 왜곡 및 수차를 보정하는 장치로, 최근 천문학 및 의료분야에서 활용되고 있다. 적응광학계의 제어는 파면을 영역별로 나누어 제어하는 zonal 방법과 모드로부터 제어하는 modal 방법이 있다. 본 연구에서는 파면 측정 장치(wavefront sensor ; WFS)인 Shack-Hartmann sensor로 측정된 파면의 기울기 정보로부터 Zernike 다항식의 계수를 계산하여 수차의 정보를 구현하고, 왜곡된 파면을 실시간으로 보정하기 위하여 Zernike 계수로부터 위상을 재구성한 후 보정장치인 변형거울을 제어하는 방법으로 파면을 보정하였다. (중략)

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2007.11a
• /
• pp.119-124
• /
• 2007

24" LCD Monitor용 LED BLU를 개발하기 위하여 1.5W R,G,G,B가 하나의 PKG에 들어있는 LED를 이용하여 Direct type의 Back Light Unit을 개발하였다. 방열 및 휘도 균일도를 고려하여 LED part를 설계, 개발 하였고 LED를 pulse로 구동하기 위하여 Switching circuit이 포함되는 구동회로 부분을 설계, 개발하였다. 또한 Color Sensor와 ICM (Integrated Color Management) IC를 사용하여 Color를 Control하는 제어회로 부분도 개발을 하였다. 제어회로 부분을 개발하기 위하여 MicroController를 사용하였으며 ICM IC와의 통신을 위한 부분도 개발하였다. 이러한 구성을 개발하여 시제품을 직접 제작하였다. 평가를 위하여 성능시험을 실시하였고 광학적인 평가도 시행하였다. 제작된 시제품은 $L558{\times}W359{\times}H30$으로 하였다. 휘도는 목표휘도를 이루었으며 균일도는 85% 이상이 되었다.

• Journal of Advanced Marine Engineering and Technology
• /
• v.35 no.8
• /
• pp.1105-1110
• /
• 2011

This paper presents an improved direct torque control based on artificial neural networks technique. The major problem that is usually associated with DTC drive is the high torque(speed) ripple. To overcome this problem a torque hysteresis band with variable amplitude is proposed based on artificial neural networks. The artificial neural networks proposed controller is shown to be able to reducing the torque(speed) ripple and dependency on motor parameter and to improve performance DTC especially at high speed and reversal running.

• Proceedings of the KIPE Conference
• /
• 2014.11a
• /
• pp.3-4
• /
• 2014

무선전력전송 회로는 부하의 변동에 따라 출력값을 알맞게 제어해 줄 필요가 있는데 송신부와 수신부 회로는 분리되어 있으므로 제어루프 또한 분리되어야 한다. 기존에는 주로 통신방식이나 부하측 변조를 이용한 1차측 제어를 사용하였다. 하지만 통신을 이용하는 경우 가격이 비싸고 시스템이 복잡하며, 부하측 변조 방식은 제어회로의 반응이 느리다는 단점이 있다. 본 논문은 2.5W급 전계결합형 무선전력회로에 대하여 LED 광학링크를 이용해 송신부의 스위칭 주파수를 제어하는 회로를 제안한다. 이 회로는 수신부에 포토다이오드와 연산증폭기를 내장하여 부하에 추가적인 배터리전원 없이 저가로 우수한 성능의 제어기를 구성할 수 있으며, 그 성능을 하드웨어로 검증하였다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.4
• /
• pp.698-706
• /
• 2016

In order to move mobile robots to desired locations in a minimum time, optimal control problems have to be solved; however, their analytic solutions are almost impossible to obtain due to robot nonlinear equations. This paper presents a method to get optimal control gains of mobile robots using genetic algorithms. Since the optimal control gains of mobile robots depend on the initial conditions, the initial condition range is discretized to form some grid points, and genetic algorithms are applied to provide the optimal control gains for the corresponding grid points. The optimal control gains for general initial conditions may be obtained by use of neural networks. So the optimal control gains and the corresponding grid points are used to train neural networks. The trained neural networks can supply pseudo-optimal control gains. Finally simulation studies have been conducted to verify the effectiveness of the method presented in this paper.