• International Journal of Aeronautical and Space Sciences
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• 제17권2호
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• pp.195-203
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• 2016

In order to overcome the influence of system stability and accuracy caused by uncertainty, estimation errors and external disturbances in Eight-Rotor MAV, L2 gain control method was proposed based on interval type II fuzzy neural network identification here. In this control strategy, interval type II fuzzy neural network is used to estimate the uncertainty and non-linearity factor of the dynamic system, the adaptive variable structure controller is applied to compensate the estimation errors of interval type II fuzzy neural network, and at last, L2 gain control method is employed to suppress the effect produced by external disturbance on system, which is expected to possess robustness for the uncertainty and non-linearity. Finally, the validity of the L2 gain control method based on interval type II fuzzy neural network identifier applied to the Eight-Rotor MAV attitude system has been verified by three prototy experiments.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.993-997
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• 1997

The continuous chip depresses the accuracy of workpieces and promotes the wear of machine tools and hunts operators. So chip control os a major problem in turning process. In this paper, a method of chip identification is develope by pyrometer. The identifier is applied in real-time control of chip pattern with adjusting feedrate.

• 제어로봇시스템학회논문지
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• 제10권12호
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• pp.1137-1147
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• 2004

In this paper, we proposed two types of adaptive control mechanism which is named HIA(Humoral Immune Algorithm) PID and CMIA(Cell-Mediated Immune Algorithm) controller based on biological immune system under engineering point of view. The HIA PID which has real time control scheme is focused on the humoral immunity and the latter which has the self-tuning mechanism is focused on the T-cell regulated immune response. To verify the performance of the proposed controller, some experiments for the control of AGV which is used for the port automation to carry container without human are performed. The experimental results for the control of steering and speed of an AGV system illustrate the effectiveness of the proposed control scheme. Moreover, in that results, proposed controllers have better performance than other conventional PID controller and intelligent control method which is the NN(neural network) PID controller.

• 제어로봇시스템학회논문지
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• 제6권6호
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• pp.441-446
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• 2000

This paper presents an approach for the prediction of optimal heating load using a diagonal recurrent neural networks(DRNN) and data base system of outdoor temperature. In the DRNN, a dynamic backpropagation(DBP) with delta-bar-delta teaming method is used to train an optimal heating load identifier. And the data base system is utilized for outdoor temperature prediction. Compared to other kinds of methods, the proposed method gives better prediction performance of heating load. Also a hardware for the controller is developed using a microprocessor. The experimental results show that prediction enhancement for heating load can be achieved with the proposed method regardless of the its inherent nonlinearity and large time constant.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2000년도 춘계학술대회논문집
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• pp.56-63
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• 2000

Abstract - In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied for the autonomous guided vehicle(AGV) driving. When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged due to the abrupt change of PID parameters since the parameters are adjusted almost randomly. To solve this problem, a neural network is used to model the plant and the parameter tuning of the model is performed by the immune algorithm. After the PID parameters are determined in this off-line manner, these gains are then applied to the plant for the on-line control using immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough intially, the weighting parameters are adjusted to be accurate through the on-line fine tuning. The computer simulation for the control of steering and speed of AGV is performed. The results show that the proposed controller has better performances than other conventional controllers.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권4호
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• pp.201-212
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• 2000

In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied for the autonomous guided vehicle(AGV) driving. When the immune algorithm is applied to the PID controller, there exists the cast that the plant is damaged due to the abrupt change of PID parameters since the parameters are adjusted almost randomly. To solve this problem, a neural network is used to model the plant and the parameter tuning of the model is performed by the immune algorithm. After the PID parameters are determined in this off-line manner, these gains are then applied to the plant for the on-line control using immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough intially, the weighting parameters are adjusted to be accurate through the on-line fine tuning. The computer simulation for the control of steering and speed of AGV is performed. The results show that the proposed controller has better performances than other conventional controllers.

• 한국항만학회지
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• 제14권2호
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• pp.187-197
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• 2000

In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied to the driving control of the autonomous guided vehicle(AGV). When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged by the abrupt change of PID parameters since the parameters are adjusted almost randomly. To solve this problem, a neural network used to model the plant and the parameter tuning of the model is performed by the immune algorithm. After the PID parameters are determined through this off-line manner, these parameters are then applied to the plant for the on-line control using immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough initially, the weighting parameters are adjusted more accurately through the on-line fine tuning. The experiment for the control of steering and speed of AGV is performed. The results show that the proposed controller provides better performances than other conventional controllers.

• 제어로봇시스템학회논문지
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• 제7권10호
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• pp.827-836
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• 2001

The PID controller has been widely applied to the most control systems because of its simple structure and east designing. One of the important points to design the PID control system is to tune the approximate control parameters for the given target system. To find the PID parameters using Ziegler Nichols(ZN) method needs a lot of experience and experiments to ensure the optimal performance. In this paper, CMIA(Cell Mediated Immune Algorithm) controller is proposed to drive the autonomous guided vehicle (AGV) more effectively. The proposed controller is based on specific immune responses of the biological immune system which is the cell mediated immunity. To verify the performance of the proposed CMIA controller, some experiments for the control of steering and speed of that AGV are performed. The tracking error of the AGV is mainly investigated for this purpose. As a result, the capability of realization and reliableness are proved by comparing the response characteristics of the proposed CMIA controllers with those of the conventional PID and NNPID(Neural Network PID) controller.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2002년도 추계공동학술대회논문집
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• pp.113-119
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• 2002

최근에, 자동화 크레인 제어 시스템은 빠른 속도와 신속한 수송이 요구되어 지고 있다 컨테이너 야드 내에서 크레인 시스템의 동작 동안, 스프레더에 매달린 컨테이너의 흔들림은 최소화로 되도록 크레인의 트롤리 위치와 와이어 로프 길이 제어가 필요하다. 크레인 시스템에서 자동 주행 제어 기술과 흔들림 방지 기술을 사용하여 무인 자동화 제어 시스템의 개발을 할 수 있는 핵심 기술이다. 그 결과 우리는 트랜스퍼 크레인 시스템 제어에서 자동 주행 제어를 위한 제어기를 설계하였다. 크레인 시스템을 통한 시뮬레이션 분석에서 다른 기존의 제어기들보다 우수한 제어 수행을 증명하였다.

• 한국통신학회논문지
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• 제30권2C호
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• pp.31-40
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• 2005

오류 역전파 알고리즘의 문제점과 ART 신경회로망의 문제점을 개선하기 위해 Jacobs가 제안한 delta-bar-delta 방법과 신경회로망을 결합한 자가 생성 지도 학습 알고리듬을 제안한다. 입력층과 은닉층에서는 ART-1과 ART-2 알고리듬을 이용하고, winner-take-all 방식은 완전 연결 구조이나 연결된 가중치만을 조정하도록 채택하였다. 실험을 위해 학생증, 주민등록증, 컨테이너의 영상으로 추출한 패턴을 신경회로망의 은닉층 노드에 대해 실험하였고, 실험결과 제안된 자기 생성 지도 학습알고리듬이 지역최소화, 학습 속도, 정체 현상이 기존의 방법보다 성능이 개선된 것을 확인하였다.