• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.330-335
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• 1998

In this paper the robust vector control method of induction motor for the purpose of improving the system performance deterioration caused by parameter variations is proposed. The estimations of the stator current and the rotor flux are obtained by the full order state observer with corrective prediction error feedback. and the adaptive scheme is constructed to estimate the rotor speed with the error signal between real and estimation value of the stator current. Adaptive sliding observer based on the variable structure control is applied to parameter identification. Consequently predictive current control and speed sensorless vector control can be obtained simultaneously regardless of the parameter variations.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.411-412
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• 2008

본 논문에서는 차세대원자로인 APR1400(Advanced Power Reactor 1400)의 출력제어방법으로 모델예측제어 알고리즘을 적용하고, 일일부하추종 운전을 하였을 때 최적의 제어기 구현을 위해 제어 로직의 주요 변수인 예측구간, 제어구간, 모델 차수의 변화에 따른 제어 성능을 평가하였다. 성능 평가는 원자로 출력제어 성능 검증시 사용하는 방법으로 제어대상인 차세대 원자로(APR1400)를 3차원 노심해석 전산코드인 MASTER(Multipurpose Analyzer for Static and Transient Effects of Reactor)로 시뮬레이션하여 제어 성능을 평가하였다.

• Proceedings of the KIPE Conference
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• 2016.11a
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• pp.131-132
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• 2016

3-레벨 중성점 다이오드 클램프(NPC) 컨버터는 high-power medium voltage(MV)에서 많이 응용된다. 하지만 3-레벨 중성점 다이오드 클램프(NPC) 컨버터는 각 스위치 소자에서 손실이 불균형하게 발생하게 되어 스위치 소자 간 성능이 불균형하게 된다. 따라서 본 논문에서는 단상 3-레벨 중성점 다이오드 클램프(NPC) 컨버터의 기존 모델 예측 제어의 스위칭 패턴을 분석 및 스위칭 순환방식을 이용한 효율적인 스위칭 상태를 갖는 모델 예측 제어를 제안한다. 이를 통해 PI 제어기 기반의 펄스 폭 변조 방법과 기존 모델 예측 제어 방법의 손실 비교를 통하여 제안하는 모델 예측 제어 성능을 검증한다.

• Proceedings of the KIPE Conference
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• 1996.06a
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• pp.17-20
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• 1996

An adaptive current control scheme of a permanent magnet (PM) synchronous motor with the simultaneous estimation of the magnitude of the flux linkage and stator resistance is proposed. The adaptive parameter estimation is achieved by a model reference adaptive system (MRAS) technique. The adaptive laws are derived by the Popov's hyperstability theory and the positivity concept. The predictive control scheme is employed for the current controller with the estimated parameters. The robustness of the proposed current control scheme is compared with the conventional one through the computer simulations.

• Proceedings of the KIPE Conference
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• 2017.11a
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• pp.25-26
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• 2017

본 논문은 양극성 직류배전망 연계를 위한 3-level NPC AC/DC 컨버터 제어 시 PI제어와 연산량 감소 모델예측제어를 혼합한 제어 기법을 제안한다. 이를 통해 기존에 비해 향상된 동특성을 확인할 수 있다. 제안하는 제어 기법을 PSIM 시뮬레이션을 통해 검증하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.5
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• pp.2414-2419
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• 2012

This paper presents an optimal operation control method for energy saving in the water reuse pumping system. A predictive horizon switching strategy is proposed to implement an optimal operation control and a linear programming (LP) algorithm is used to solve optimal problems in each time step. Energy costs are calculated for electricity on both TOU in the light, heavy, and maximum load time period and peak charges. The optimal operation in water reuse pumping systems is determined to reduce the TOU and peak costs. The simulation results show a power energy saving for water reuse pumping systems and power stability improvement.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.22-24
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• 2006

The installations of power quality monitoring system have increased drastically over the past several decades. These systems have been effectively used to monitor, analyze and diagnose the conditions of power system, and furthermore can be used to improve the present asset maintenance policy, scheduled (time-based) method, into the advanced, cost-effective and labor-effective maintenance methods, such as condition-based maintenance, predictive maintenance and reliability centered maintenance. As an approach to this, this paper introduces the statistical methods, three kinds of control charts (Shewhart chart, CUSUM chart and EWMA chart), and discusses the applicability of these methods to recognize the changing trends of power quality indices and to estimate the system's condition, using Matlab.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1031-1033
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• 1996

Now is an automation age. Therefore it is required that machine can do work which was done by men. Artificial Neural Network was developed by the necessity of this purpose. This paper shows a Predictive Control with a Neural Network. The Neural Network learns an Inverted Pendulum in various situations. Then, it has a power to predict the next state after accept the current state. And the Neural Network directs the Bang-Bang Controller to give input to a plant. It seems like that a human expert looks the state of a plant and then controls the plant. It is used a Feedforward Neural Network and shown control state according to the learning. We could get a satisfactory results after complete learning.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.261-264
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• 1993

This paper proposes an adaptive generalized predictive control with feedforward algorithm for steam generator level control in nuclear power plant. The proposed algorithm is shown that the parameters of N-step ahead predictors can be obtained using the parameters of one-step ahead predictor which is derived from plant model with feedforward. Using this property the proposed scheme is an adaptive algorithm which consists of GPC method and the recursive least squares algorithm for identifying the parameters of one-step ahead predictor. Also, computer simulations are performed to evaluate the performance of proposed algorithm using a mathematical model of PWR steam generator Simulation results show good performances for load variation. And the proposed algorithm shows better responses than PI controller does.

• Journal of Drive and Control
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• v.21 no.3
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• pp.28-35
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• 2024

This study compared performances of PID (Proportional Integral Derivative), SMC (Sliding Mode Control), and MPC (Model Predictive Control) strategies applied to a 2DOF (Degree Of Freedom) drone tracking robot. The developed 2DOF robot utilized a depth camera with an IMU (Inertial Measurement Unit), laser pointers, and servo motors to rapidly detect and track objects. Image processing was conducted using the YOLO deep learning model. Through this setup, controllers were attached to the robot to track random drone movements, comparing performances in terms of accuracy and energy consumption. This study revealed that while SMC demonstrated precise tracking without deviating from the path, both PID and MPC controllers showed deviations. Performance-wise, SMC is superior. However, considering economic aspects, PID is more advantageous due to its lower power consumption and relatively minor tracking errors.