• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.865-870
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• 2003

A motion controller has been used variously in industry such as semiconductor manufacture equipment, industrial robot, assembly/conveyor line applications and CNC equipment. There are several types of controller in motion control. One of these is a PC-based motion controller such as PCI or ISA, and another is stand-alone motion controller. The PC bus-based motion controller is popular because of improving bus architectures and GUI (Graphic User Interface) that offer convenience of use to user. There are some problems in this. The PC bus-based solution allows for only one of the form factors, so it has a poor flexibility. The overall system package size is bigger than other motion control system. And also, additional axes of control require additional slot, however the number of slots is limited. Furthermore, unwieldy and many wirings come to connect plants or I/O. The stand-alone motion controller has also this limit of axes of control and wiring problems. To resolve these problems, controller must have capability of operating as stand-alone devices that resides outside the computer and it needs network capability to communicate to each motion device. In this paper, a network-based stand-alone motion system is proposed. This system integrates PC and motion controller into one stand-alone motion system, and uses CAN (Controller Area Network) as network protocol. Single board computer that is type of 3.5" FDD form factor is used to reduce the system size and cost. It works with Windows XP Embedded as operating system. This motion system operates by itself or serves as master motion controller that communicates to slave motion controller. The Slave motion controllers can easily connect to master motion system through CAN-network.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.8
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• pp.898-906
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• 1999

A control structure is introduced for the purpose of rejecting periodic (or repetitive) disturbances on a tracking system. The objective of the proposed structure is to drive the output of the system to the reference input that will result in perfect following without any changing the inner configuration of the system. The structure includes an adaptation block which learns the dynamics of the periodic disturbance and forces the interferences, caused by disturbances, on the output of the system to be reduced. Since the control structure acquires the dynamics of the disturbance by on-line adaptation, it is possible to generate control signals that reject any slowly varying time-periodic disturbance provided that its amplitude is bounded. The artificial neural network is adopted as the adaptation block. The adaptation is done at an on-line process. For this , the real-time recurrent learning (RTRL) algoritnm is applied to the training of the artificial neural network.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.401-403
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• 1996

In this paper, a new control strategy is presented that combines sliding mode control theory with a neural network. Sliding mode control theory requires the complete knowledge of the dynamics of the controlled system. However, in practice, one often bas only a small number of state measurements. This could be a serious limitation on the practical usefulness of sliding mode control theory. A multilayer neural network is employed to solve this kind of problem. The neural network serves as a compensator without a prior knowledge about the system. The proposed control algorithm is applied to a class of uncertain nonlinear system. The robustness against parameter uncertainty, nonlinearity and external disturbances, and the effectiveness is verified by the simulation results.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.179-180
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• 2006

The real time monitoring of the power systems covering wide area are essential for the stable operation and control of the power system. Synchronized phasor measurement is a key for the precise monitoring and control of the power systems. In this paper, to suggest an appropriate network topology of Power Infrastructure Defense System(PIDS) and to estimate the maximum network bandwidth with using the network analyzer, we simulate a PIDS and analyze the network traffic.

• 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.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2140-2142
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• 2003

Recently, TCP/IP on Ethernet protocol is in use environment of LAN have developed a single chip by hardware. Therefore, the study about the single chip applies to the system control application like information electronic appliances, manufacturing automation machine has been made progress. This paper is the development of experimental Client node and Serve node that can transfer input-output data needed on Network Control System Client node is a sensor part of control system, that is, an analog signal is applicable to output data convert AD through LAN. Server node data sended in client convert DA, and then it is applicable to driver of Control System, so it achieves its part. In this study, is prove that using TCP/IP construct Network Control System.

• International Journal of Control, Automation, and Systems
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• v.4 no.2
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• pp.172-177
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• 2006

In the networked control system (NCS), the uncertain network-induced delay and nonlinear controlled object are the main problems, because they can degrade the performance of the control system and even destabilize it. In this paper, a class of uncertain and nonlinear networked control systems is discussed and its sufficient condition for the robust asymptotic stability is presented. Further, the maximum network-induced delay that insures the system stability is obtained. The Lyapunov and LMI theorems are employed to investigate the problem. The result of an illustrative example shows that the robust stability analysis is sufficient.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1478-1481
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• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network controller. The back-propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back-propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

• ETRI Journal
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• v.39 no.4
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• pp.525-534
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• 2017

A hardware-acceleration architecture that separates virtual network functions (VNFs) and network control (called HSN) is proposed to solve the mismatch between the simple flow steering requirements and strong packet processing abilities of software-defined networking (SDN) forwarding elements (FEs) in SDN/network function virtualization (NFV) architecture, while improving the efficiency of NFV infrastructure and the performance of network-intensive functions. HSN makes full use of FEs and accelerates VNFs through two mechanisms: (1) separation of traffic steering and packet processing in the FEs; (2) separation of SDN and NFV control in the FEs. Our HSN prototype, built on NetFPGA-10G, demonstrates that the processing performance can be greatly improved with only a small modification of the traditional SDN/NFV architecture.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.267-270
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• 1997

As an approach to develope a control system with robustness in changing control environment conditions, this paper will propose a robot manipulator control system using multilayer neural network and immune algorithm. The proposed immune algorithm which has the characteristics of immune system such as distributed and anomaly detection, probabilistic detection, learning and memory, consists of the innate immune algorithm and the adaptive immune algorithm. We will demonstrate the effectiveness of the proposed control system with simulations of a 2-link robot manipulator.