• 전력전자학회논문지
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• 제6권1호
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• pp.49-56
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• 2001

본 논문은 신경회로망에 근거한 교류서보전동기의 제어를 위해 새로운 기법을 제안하고자 한다. 일반적인 제어 기법을 사용하는 대선 선경회로망 PID제어가가 교류서보전동기 제어를 위해 이용되었다. 서보시스템에서 널리 이용되는 제어는 PID제어이며, 이는 간단한 구조와 안전성과 신뢰성을 지니고 있다. 그러나 복잡한 사스템에서 문제점을 나타내므로 불확실한 파라미터의 상태에서는 불완전한 동작을 나타내고 있다. 본 논문에서는 PID제어기의 개선된 제어 알고리즘을 제안하여 교류서보전동기의 시스템을 신경회로망제어를 이용한 PID제어기에 의해 구동시켜 단계적인 제어를 시뮬레이션과 실험에 의해서 제안한 제어알고리즘의 타당성을 입증하고자 한다.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2001년도 하계 학술대회 논문집(KISPS SUMMER CONFERENCE 2001
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• pp.121-124
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• 2001

In industrial actual control system, PID controller has been used with its high delicate control system in position control system. PID controller has simple structure and superior ability in several characteristics. When the response of system is changed by delay time, variable load , disturbances and external environment, control gain of PID controller must be readjusted on the system dynamic characteristics. Therefore, a control ability of PID controller is degraded when th control gain is inappropriately determined. When the response characteristic of system is changed under a condition, control gain of PID controller must be changed adaptively to be a waited response of system. In this paper an adaptive-tuning type PID controller is constructed by self-recurrent Neural Network(SRNN). applying back-propagation(BP) algorithm. Form the result of computer simulation in the proposed controller, its usefulness is verified.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2001년도 추계종합학술대회
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• pp.380-385
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• 2001

본 논문에서는 기존의 역전파 알고리즘(Back-propagation Algorithm)의 문제점인 지역 최소점(Local Minimum point) 빠질 위험을 제거함과 동시에 학습속도(learning-speed)를 빠르게 하기 위해서 모먼팀을 갖는 PID 역전파 알고리듬(PIDBPMT: PID Back-Propagation algorithm with Momentum Term)을 제안하여 모먼텀 항을 갖는 신경망 PID 제어기를 설계하였다. 그 제어기를 D.C 서보 모터를 구동원으로 하는 위치제어계에서 적용하여 시뮬레이션으로 그 성능을 검정하였다.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권6호
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• pp.433-439
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• 2004

The Helicopter Simulator System is non-linear and complex. Futhermore, because of absence of its accurate mathematical model, it is difficult to control accurately its attitudes such as elevation angle and azimuth one. Therefore, we proposed a Hybrid GA-PID WAVENET(Genetic Algorithm Proportional Integral Derivative Wavelet Neural Network)control technique to control efficiently these angles. The proposed Hybrid GA-PID WAVENET is made through the following process. First, the WAVENET fundamental functions are defined. And their dilation and translation values are adjusted by GA to construct the optimal WAVENET controller. Secondly, the proportional, integral, and derivative gain coefficients of PR controller are tuned optimally. Finally, WAVENET controller which has a good transient characteristic and GA-PE controller which has a good steady state characteristic is adequately combined in hybrid type. Through the computer simulations, it is proved that the Hybrid GA-PE WAVENET control technique has a more excellent dynamic response than PID control technique and GA-PID one.

• 조명전기설비학회논문지
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• 제20권7호
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• pp.53-57
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• 2006

PID 제어기는 산업자동화 설비에 널리 쓰이고 있다. 하지만 시스템 특성이 간헐 또는 연속적으로 변화할 때에 정밀제어를 위한 새로운 매개변수 결정이 쉽지 않다. 이를 해결하기 위한 방법으로 본 논문에서는 PID 제어기와 같은 기능을 갖는 신경망이득 자동조절기를 제안 하였다. 시스템의 적절한 궤환제어 이득은 델타 학습규칙에 의해서 결정된다. 제안된 신경망이득 자동조절기의 기능은 유도 전동기의 속도제어 실험에 의해 확인하였다.

• 전기학회논문지
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• 제56권5호
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• pp.966-972
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• 2007

This paper presents position control of nonlinear three-dimensional crane systems using neural network approach. Such crane system generally includes very complicated characteristic dynamics and mechanical framework such that its mathematical model is expressed by strong nonlinearity. This leads difficulty in control design for the systems. We linearize the nonlinear system model to construct PID control applying well-known linear control theory and then neural network is utilized to compensate system perturbation due to linearization. Thus, control input of the crane system is composed of nominal PID and neural output signals respectively. Our method illustrates simple design procedure, but system perturbation and modelling error are overcome through a neural compensator. As well. adaptive neural control is constructed from online learning. Computer simulation demonstrates our control approach is superior to the classic control systems.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2009년도 추계학술발표논문집
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• pp.630-632
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• 2009

The parameters of PID controller should be readjusted whenever system character change. In spite of a rapid development of control theory, this work needs much time and effort of expert. In this paper, to resolve this defect, after the sample of parameters in the changeable limits of system character is obtained, these parametrs are used as desired values of back propagation learning algorithm, also neural network auto tuner for PID controller is proposed by determing the optimum structure of neural network. Simulation results demonstrate that auto-tuning proper to system character can work well.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2000년도 하계종합학술대회논문집
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• pp.105-108
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• 2000

Many control techniques have been proposed in order to improve the control performance of discrete-time domain control system. In the position control system using a DC servo motor as a driver, the response-characteristic of system is controlled by the PID controller. In the PID control system, the transient response characteristic is more increased and settling time gets longer if gains of PID controller are unsuitable. In this paper, therefore, a expended PID control system is constructed by inserting a pre-compensator at output terminal of PID controller. It is implemented by using the double layers neural network. Form the results of computer simulation in the proposed control algorithm, its usefulness is verified.

• 융합신호처리학회논문지
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• 제5권2호
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• pp.138-142
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• 2004

본 논문은 모델추종 제어을 위한 PID제어기법을 제안한다. 이산시간영역에서 제어성능을 개선하기 위해 제안하였고, PID 제어계에 새로운 전치 보상기를 삽입하여 모델추종제어계가 되도록 하였다. 외란이나 부하변동에 의해서 계의 응답이 변할 때 PID 제어기의 이득을 재조정할 필요가 있다. PID 제어계에서 각 PID 이득이 제어계의 성능을 크게 좌우하게 되므로 신경망을 PID제어기에 결합하여 제어계의 성능을 향상시켰고 제안한 제어계에서 PID제어기의 이득은 역전파 알고리즘에 의해 자동적으로 조정되어지도록 하였다. 모델추종 제어계의 제어성능을 확인하기 위하여 제어대상을 직류 서보 전동기의 각 위치로 하였다. 이것을 위치 제어계에 적용하여 실험을 통해 그 성능을 증명하였다.

• 제어로봇시스템학회논문지
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• 제13권4호
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• pp.296-303
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• 2007

This paper proposes a new width control system composed of an ANWC(Automatic Neural network based Width Control) and a fuzzy-PID controller in hot strip finishing mills which aims at obtaining the desirable width. The ANWC is designed using a neural network based width prediction model to minimize a width variation between the measured width and its target value. Input variables for the neural network model are chosen by using the hypothesis testing. The fuzzy-PlD control system is also designed to obtain the fast looper response and the high width control precision in the finishing mill. It is shown through the field test of the Pohang no. 1 hot strip mill of POSCO that the performance of the width margin is considerably improved by the proposed control schemes.