• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.142-142
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• 2000

During the operation of crane system in container yard, it is necessary to control the crane trolley position so that the swing of the hanging container is minimized. Recently an automatic control system with high speed and rapid transportation is required. Therefore, we designed a controller to control the crane system with disturbances. In this paper, Ive present the neural network two degree of freedom PID controller to control the swing motion and trolley position. Then we executed the computer simulation to verify the performance of the proposed controller and compared the performance of the neural network PID controller with our proposed controller in terms of the rope swing and the precision of position control . Computer simulation results show that the proposed controller has better performances than neural network PID with disturbances.

• 조명전기설비학회논문지
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• 제18권3호
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• pp.95-100
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• 2004

최근, 비선형시스템인 DC-DC 컨버터의 출력전압제어를 위해서 많은 제어방법들이 연구되고 있으며, 본 논문은 Buck type DC-DC 컨버터에 슬라이딩 모드 제어기와 뉴럴 네트워크 제어기를 이용한 제어 알고리즘을 제시하였고, 또한 컨버터의 이론해석을 통한 이론특성과 Psim을 이용한 시뮬레이션특성, DSP(TMS320C32)를 이용한 실험특성을 비교 검토하여 제안한 방법의 유용성을 입증하였다. 기존의 히스테리시스 제어기를 이용한 제어방법과 제안한 슬라이딩 모드 제어기와 뉴럴 네트워크 제어기를 이용한 제어방법과 비교한 결과 제안한 제어기가 우수한 특성을 얻었으며, 향후, 다른 전력변환장치에 유용하게 적용될 것으로 생각된다.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2001년도 하계 학술대회 논문집(KISPS SUMMER CONFERENCE 2001
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• pp.141-144
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• 2001

본 논문에서는 신경망제어기의 출력을 보상하는 퍼지보상기를 갖는 신경망제어기에 관하여 제안하였다. 학습이 완료된 신경망제어기를 사용하더라도 예상치 못한 외란으로 인해 플랜트의 출력이 좋지 못한 경우가 있는데, 이것을 적절하게 조절해 주기 위해 퍼지보상기를 사용하여 원하는 결과를 얻을 수 있도록 하였다. 그리고 플랜트의 역 모델 신경망을 학습시킨 결과를 이용하여 주 신경망의 가중치를 변경시킴으로서 원하는 플랜트의 동적 특성을 얻게 된다. 세안한 제어기의 성능을 확인하기 위해 유압 서보시스템을 대상으로 DSP 프로세서를 사용하여 구현한 후 실험결과를 관찰하였다.

• International Journal of Control, Automation, and Systems
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• 제6권3호
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• pp.453-459
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• 2008

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the 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 for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.

• 한국정밀공학회지
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• 제20권7호
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• pp.155-162
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• 2003

We propose a new technique far real-tine controller design of a autonomous cruise mobile robot with three drive wheels. The proposed control scheme uses a Caussian function as a unit function in the fuzzy neural network. and a back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-foray. The control performance of the proposed controller is illustrated by performing the computer simulation for trajectory tracking of the speed and azimuth of a autonomous cruise mobile robot driven by three independent wheels.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.1697-1700
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• 2003

We propose a new technique for real-time controller design of a autonomous cruise mobile robot with three drive wheels. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and a back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The control performance of the proposed controller is illustrated by performing the computer simulation for trajectory tracking of the speed and azimuth of a autonomous cruise mobile robot driven by three independent wheels.

• 한국정보처리학회논문지
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• 제3권6호
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• pp.1473-1482
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• 1996

본 논문은 순수 퍼지 제어기와 신경망을 도입한 퍼지 신경망 제어기의 응답 특성 에 대해 연구 고찰하였다. 퍼지 제어기는 초기치에서 과도 응답 특성이 우수하고 외 란에도 강한 장점을 가지고 있지만 목표치에서 약간의 오차가 항상 있다. 이러한 정 상 상태의 오차를 제거하기 위한 여러 방법이 소개되고 있다. 이런 관점에서 본 논문 은 학습 능력이 있는 신경망(neural network)을 응용한 퍼지 신경망 제어기(fuzzy neural network controller)을 제안하였다. 이 퍼지 신경망제어기는 목표치에서 오차 가 발생하는 기존 퍼지 제어기의 단점을 보완하여 오차없이 목표치에 정확하게 수렴 하여 정밀 제어가 가능하게 된다. 이와 같은 두 제어기 간의 목표치에 수렴하는 응답 특성은 실험을 통해 비교하였다.

• 한국지능시스템학회논문지
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• 제9권5호
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• pp.463-471
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• 1999

This paper presents a vector controlled induction motor is implemented by neural networks system compared with PI controller for the speed control. The design employed the training strategy with Neural Network Controller(NNC) and Neural Network Emulator(NNE) for speed. In order to update the weights of the controller First of all Emulator updates its parameters by identifying the motor input and output next it supplies the error path to the output stage of the controller using backpropagation algorithm, As Controller produces an adequate output to the system due to neural networks learning capability Vector controlled induction motor characteristics actual motor speed with based on neural network system follows the reference speed better than that of linear PI speed controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1561-1564
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• 1997

In the study, simulation result was studied by connecting PID controller in series to the established Neural Networks Controller. Neural Network model is composed of two layers to evaluate tracking performance improvement. The reqular dynamic characteristics was also studied for the expected error to be minimized by using Widrow-Hoff delta rule. As a result of the study, We identified that tracking performance inprovement was developed more in case of connecting PID than Neural Network Contoller and that tracking plant parameter in 251 sample was approached rapidly case of time variable.

• 한국정밀공학회지
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• 제13권4호
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• pp.75-86
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• 1996

In this paper, we propose a new fuzzy-neural network control scheme for the speed and azimuth control of a mobile robot. The proposed control scheme uses a gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the frame-work of the specialized learning architecture. It is proposed a learning controller consisting of two fuzzy-neural networks based on independent reasoning and a connection net woth fixed weights to simply the fuzzy-neural network. The effectiveness of the proposed controller is illustrated by performing the computer simulation for a circular trajectory tracking of a mobile robot driven by two independent wheels.