• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.96.2-96
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• 2001

This paper presents a control method for the experimental mobile vehicle. By merging the advantages of neural network, adaptive and fuzzy control, neural network-based adaptive fuzzy control is proposed. It can deal with a large amount of training data by neural network, from these data producing more accurate fuzzy rules by adaptive control, and then controlling the object by fuzzy control. This is not the simple combination of the three methods, but merging them into one control system Experiments and some future considerations are given.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.120.2-120
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• 2002

In this paper auto-tuning scheme of weight function in the neural networks has been suggested by immune algorithm for nonlinear process. A number of structures of the neural networks are considered as learning methods for control system. A general view is provided that they are the special cases of either the membership functions or the modification of network structure in the neural networks. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. Also. It can provi..

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.316-321
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• 2003

This paper presents a design method of the wavelet neural network based controller using direct adaptive control method to deal with a stable intelligent control of chaotic systems. The various uncertainties, such as mechanical parametric variation, external disturbance, and unstructured uncertainty influence the control performance. However, the conventional control methods such as optimal control, adaptive control and robust control may not be feasible when an explicit, faithful mathematical model cannot be constructed. Therefore, an intelligent control system that is an on-line trained WNN controller based on direct adaptive control method with adaptive learning rates is proposed to control chaotic nonlinear systems whose mathematical models are not available. The adaptive learning rates are derived in the sense of discrete-type Lyapunov stability theorem, so that the convergence of the tracking error can be guaranteed in the closed-loop system. In the whole design process, the strict constrained conditions and prior knowledge of the controlled plant are not necessary due to the powerful learning ability of the proposed intelligent control system. The gradient-descent method is used for training a wavelet neural network controller of chaotic systems. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with application to the chaotic systems.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2008년도 정보통신설비 학술대회
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• pp.364-367
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• 2008

Many different types of network equipments are deployed in a large-scale IP network. In this operating environment, network service providers suffer from difficulty in controlling various equipments simultaneously in case network faults happen in their overall or regional network due to physical link failure or abnormal traffic. This paper presents a policy-based methodology to control many different types of network equipments at the same time in abnormal cases. The key idea is that NMS(Network Management System) keeps vendor-neutral control policies in normal times and that when an abnormal case occurs in network, NMS transforms the selected policy into vendor-specific control commands and enforces them to various equipments simultaneously.

• 한국산학기술학회논문지
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• 제19권4호
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• pp.664-673
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• 2018

산업제어시스템(ICS, Industrial Control System)은 과거에는 폐쇄 네트워크로 운영되었으나 최근에는 정보통신 서비스와 연결되어 운영되면서 사이버 공격으로 인한 피해로 이어지고 있다. 이에 대한 대책으로 정보통신기반보호법이 제정되고 보안가이드라인이 배포되고 있지만 제어망에서 업무망으로의 일방향 정책만 있어 다양한 실제 제어 환경에 대한 보안가이드가 필요하며 국제 표준으로 IEC 62443의 경우 산업제어시스템 참조 모델을 정의하고 침입 차단 시스템을 이용한 영역 보안 모델을 제시하고 있으나 폐쇄 네트워크로 운영되는 산업제어망을 침입 차단 시스템만으로 외부 네트워크와과 연계하기에는 부담이 있다. 본 논문에서는 국내외 산업제어시스템 보안 모델 및 연구 동향을 분석하고 다양한 국내 산업제어망의 실제 연동 환경에 적용할 수 있는 산업제어시스템 보안 모델을 제안한다. 또한 현재 상용 경계 보안 제품들인 침입 차단 시스템, 산업용 침입 차단 시스템, 망 연계 장비, 일방향 전송 시스템 등의 보안성을 분석하였다. 이를 통하여 국내 구축 사례와 정책에 대한 비교를 통해 보안성이 향상되는 것을 확인하였다. 4차 산업혁명 시대를 맞이하여 스마트 팩토리, 스마트 자동차, 스마트 플랜트 등 다양한 산업제어 분야에 대한 보안 관리 방안에 적용할 수 있을 것이다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.131-134
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• 1997

This paper presents a new approach to the design of intelligent control system for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and 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 performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1961-1965
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• 1991

This paper deals with an automatic and precision alignment technique for proximity printing in x-ray lithography, using two pairs of moire gratings, with moire signals from each pair being 180.deg. out of phase with each other. We constructed an automatic and precision alignment experimental system which could measure both transmitted moire signals and reflected moire signals at the same time. The automatic alignment was achieved using transmitted moire signals and also reflected moire signals as a control signal for a stage driver. The alignment position of the system was monitored not only by a control signal but also by a non-control signal. The effect of transmitted and reflected moire signals upon alignment accuracy was discussed. We concluded that the technique using diffracted moire signals is a viable automatic and precision alignment technique.

• 제어로봇시스템학회논문지
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• 제6권8호
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• pp.689-696
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• 2000

In this paper we propose an eminent controller for wheeled mobile robots. This controller consists of an input-output linearization controller trying to stabilize the system and a neural network controller to compensate for uncertainties. The uncertainties are divided into two parts. First unstructured uncertainties include the elements related with system order such as friction disturbance. Second structure uncertainties are the incorrect system parameters A neural network structure of the proposed overall controller learns structural errors of the wheeled mobile robots with uncertainties and includes the neural network output. This controller learns quickly the model and has good tracking performance Simulation results show that the proposed controller is more efficient than analog controllers.

• Nuclear Engineering and Technology
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• 제35권3호
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• pp.243-250
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• 2003

Nuclear power plants (NPPs) will be highly connected network enabled systems in the future. Using the network and web enabled tools, NPPs will be remotely monitored by operators at any time from any place connected to the network via a general web browser. However, there will be two major issues associated with this implementation. The first is the security issue. Only the authorized persons need to be allowed to access the plant since NPP is a safety-critical system. However, the web technology is open to the public. The second is the network disturbance issue. If operators can not access the plant due to network disturbances, the plant will come into the out-of-control situation. Therefore, in this work, we performed a conceptual design of a web-based remote monitoring and control system (RMCS) considering these issues.

• 한국정밀공학회지
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• 제30권8호
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• pp.824-830
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• 2013

We propose a tracking control system for butterfly valves. A sliding mode controller with a fuzzy-neural network algorithm was applied to the design of the tracking control system. The control scheme used the real-time update law for the unmodeled system dynamics using a fuzzy-neural network algorithm. The performance of the proposed control system was assessed through a range of experiments.