• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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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.

• Proceedings of the KIEE Conference
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• 2002.07d
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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.

• The KIPS Transactions:PartC
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• v.13C no.5 s.108
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• pp.549-558
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• 2006

For activating home network, the security service is positively necessary and especially the access control supports secure home network services and differentiated services. But, the existing security technology for home network seldom consider access control or has a architecture to be dependent on specific middleware. Therefore, in this paper we propose a scheme to support integrated access control in home network to use XACML, access control standard of next generation, to have compatability and extensibility and we design and implement XACML access control system based on this. we also had m access control experiment about various policy to connect developed XACML access control system with the UPnP proxy based on OSGi in order to verify compatability with existing home network system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.3
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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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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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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• 2005.06a
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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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• v.17 no.2
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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.

• The KIPS Transactions:PartA
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• v.8A no.1
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.9
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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.