• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.436-439
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• 1995

In this paper we present a method to construct fuzzy model with multi-dimension input membership function, which can construct fuzzy inference system on one node of the network directly. This method comes from a common framework called Universal Learning Network (ULN). The fuzzy model under the framework of ULN is called Universal Learning Network-based Fuzzy Inference System (ULNFIS), which possesses certain advantages over other networks such as neural network. We also introduce how to imitate a real system with ULN and a control scheme using ULNFIS.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2445-2447
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• 2002

In this paper, CAN-based feedback control system is proposed for the pressurization system of korean high-speed train. The control performance of the system is evaluated. According to the requirement of the pressurization system A process model considering network delay and an adaptive PID control method based on the process model are proposed here. And it is shown that the proposed adaptive PID control method considering the network delay has on adequate feature compared to some other existing methods consequently it can be considered to be applied the pressurization system of korean high-speed train.

• 정보처리학회논문지C
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• 제13C권5호
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• pp.549-558
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• 2006

홈 네트워크가 활성화되기 위해서는 보안 서비스의 제공이 필수적이며 특히 사용자에 대한 접근제어는 안전하고 차별화 된 홈 네트워크 서비스의 제공을 가능하게 한다. 그러나 기존의 흠 네트워크 보안 기술은 접근제어를 거의 고려하지 않거나 특정 미들웨어에 종속적인 구조를 가진다. 따라서 본 논문에서는 상호 호환성 및 확장성이 뛰어난 차세대 접근제어 표준인 extensible Access Control Markup Language(XACML)를 이용하여 흠 네트워크에서 통합적인 접근제어를 제공하기 위한 방안을 제시하고 이를 바탕으로 XACML 접근제어 시스템을 설계하고 구현한다. 또한 구현된 XACML 접근제어 시스템을 OSGi기반 UPnP 프락시 시스템에 적용하여 다양한 정책에 대한 실험을 수행함으로써 기존 홈 네트워크 시스템과의 호환성을 검증하였다.

• 대한전기학회논문지
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• 제43권5호
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• pp.838-847
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• 1994

Although there is an analytical proof of modeling capability of the neural network, the convergency error in nonlinearity modeling is inevitable, since the steepest descent based practical larning algorithms do not guarantee the convergency of modeling error. Therefore, it is difficult to apply the neural network to control system in critical environments under an on-line learning scheme. Although the convergency of modeling error of a neural network is not guatranteed in the practical learning algorithms, the convergency, or boundedness of tracking error of the control system can be achieved if a proper feedback control law is combined with the neural network model to solve the problem of modeling error. In this paper, the neural network is introduced for compensating a system uncertainty to control a nonlinear dynamic system. And for suppressing inevitable modeling error of the neural network, an iterative neural network learning control algorithm is proposed as a virtual on-line realization of the Adaptive Variable Structure Controller. The efficiency of the proposed control scheme is verified from computer simulation on dynamics control of a 2 link robot manipulator.

• 제어로봇시스템학회논문지
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• 제9권3호
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• pp.196-203
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• 2003

Recent increase in accessibility to the internet makes it easy to use the internet-connected devices. The internet could allow any user can reach and command any device that is connected to the network. But these teleoperation systems using the internet connected device have several problems such as the network time delay, data loss and development cost of an application for the communication with each other. One feasible solution is to use local and external network line for the network time delay, transmission control protocol for data loss and Java language to reduce the development period and cost. In this study, web-based remote control system using Java language is newly proposed and implemented to a pneumatic servo control system to solve the time delay, data loss and development cost. We have conducted several experiments using pneumatic rodless cylinder through the internet and verified that the proposed remote control system was very effective.

• 대한전기학회논문지:전력기술부문A
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• 제48권3호
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• pp.286-293
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• 1999

A new control scheme using a torsional torque estimator based on a neural network is proposed and investigated for improving control characteristics of the high-performance motion control system. This control method presents better performance in the corresponding speed vibration response, compared with the disturbance observer-based control method. This result comes from the fact that the proposed neural network estimator keeps the self-learning capability, whereas the disturbance observer-based torque estimator with low pass filter should dbjust the time constant of the adopted filter according to the natural resonance frequency detemined by considering the system parameters varied. The simulation results shows the validity of the proposed control scheme.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1598-1601
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• 2005

In this paper, a heterogeneous home network control system using HNCP is proposed and implemented. A power line and 802.15.4 are used as media for the system. Information about home environment gathered by sensors is transferred to a power line connected device through the 802.15.4. HNCP stimulate the home network based on the both media. Sensor device definition for the HNCP address and message set is proposed. TinyOS supports the HNCP stack on the wireless sensor board. The home network control system implemented with these techniques has a benefit of user friendly operation of home appliances based on the sensing data. Implementation and experiment shows validity of the system.

• 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.

• 정보처리학회논문지A
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• 제8A권1호
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• pp.56-62
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• 2001

This paper presents a neural network based variable structure control scheme for nonlinear systems. In this scheme, a set of local variable structure control laws are designed on the basis of the linear models about preselected representative points which cover the range of the system operation of interest. From the combination of the set of local variable structure control laws, neural networks infer the approximate control input in between the operating points. The neural network based variable structure control alleviates the effects of model uncertainties, which cannot be compensated by the control techniques using feedback linearization. It also relaxes the discontinuity in the system’s behavior that appears when the control schemes based on the family of the linear models are applied to nonlinear systems. Simulation results of a ball and beam system, to which feedback linearization cannot be applied, demonstrate the feasibility of the proposed method.

• 제어로봇시스템학회논문지
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• 제16권9호
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• pp.898-904
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• 2010

Unmanned autonomous forklifts have a great potential to enhance the productivity of material handling in various applications because these forklifts can pick up and deliver loads without an operator and any fixed guide. There are, however, many technical difficulties in developing such forklifts including localization, map building, sensor fusion, control and so on. Implementation, which is often neglected, is one of practical issues in developing such an autonomous device. This is because the system requires numerous sensors, actuators, and controllers that need to be connected with each other, and the number of connections grows very rapidly as the number of devices grows. Another requirement on the integration is that the system should allow changes in the system design so that modification and addition of system components can be accommodated without too much effort. This paper presents a network-based distributed approach where system components are connected to a shared CAN network, and control functions are divided into small tasks that are distributed over a number of microcontrollers with a limited computing capacity. This approach is successfully applied to develop an unmanned forklift.