• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.769-772
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• 2003

Middleware enables different networking devices and protocols to inter-operate in ubiquitous home network environments. The UPnP(Universal Plug and Play) middleware, which runs on a PC and is based on the IPv4 protocol, has attracted much interest in the field of home network research since it has versatility. The UPnP, however, cannot be easily accessed via the public Internet since the UPnP devices that provide services and the Control Points that control the devices are configured with non-routable local private or Auto IP networks. The critical question is how to access UPnP network via the public Internet. The purpose of this study is to deal with the non-routability problem in local private and Auto IP networks by improving the conventional Control Point used in UPnP middleware-based home networks. For this purpose, this paper proposes an improved Control Point for accessing and controlling the home network from remote sites via the public Internet, by adding a web server to the conventional Control Point. The improved Control Point is implemented in an embedded GNU/Linux system running on an ARM9 platform.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.82-85
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• 2004

This paper proposes the development of delay compensator to minimize performance degradation caused by time delays in network-based real-time control systems. The delay compensator uses the time-stamp method as a direct delay measuring method to measure time delays generated between network nodes. The delay compensator predicts the network time delays of next period in the views point of time delays and minimizes performance degradation from network through considering predicted time delays. Control output considering network time delays is generated by the defuzzification of probable time delays of next period. The time delays considered in the delay compensator are modeled by using a timed Petri net model. The proposed delay prediction mechanism for the delay compensator is evaluated through some simulation tests by measuring deviation of the predicted delays from simulated delays.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.831-833
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• 2004

In this paper, an obstacle avoidance algorithm is proposed for a network-based robot considering network delay by distribution. The proposed algorithm is based on the VFH(Vector Field Histogram) algorithm, and for the network-based robot system, in which it is assumed robot localization information is transmitted through network communication. In this paper, target vector for the VFH algorithm is estimated through the robot localization information and the measurement of its delay by distribution. The delay measurement is performed by time-stamp method. To synchronize all local clocks of the nodes distributed on the network, a global clock synchronization method is adopted. With the delay measurement, the robot localization estimation is performed by calculating the kinematics of the robot. The validation of the proposed algorithm is performed through the performance comparison of the obstacle avoidance between the proposed algorithm and the existing VFH algorithm on the network-based autonomous mobile robot.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.296-303
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• 2007

This paper proposes a new width control system composed of an ANWC(Automatic Neural network based Width Control) and a fuzzy-PID controller in hot strip finishing mills which aims at obtaining the desirable width. The ANWC is designed using a neural network based width prediction model to minimize a width variation between the measured width and its target value. Input variables for the neural network model are chosen by using the hypothesis testing. The fuzzy-PlD control system is also designed to obtain the fast looper response and the high width control precision in the finishing mill. It is shown through the field test of the Pohang no. 1 hot strip mill of POSCO that the performance of the width margin is considerably improved by the proposed control schemes.

• Journal of the Korean Society of Safety
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• v.21 no.4 s.76
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• pp.25-32
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• 2006

LonWorks over IP(LonWorks-IP) virtual device network(VDN) is an integrated form of LonWorks device network and IP data network. In especially real-time distributed servo applications on the factory floor, timely response is essential for predictive and preventive maintenance. The time delay in servo control on LonWorks-IP based VDN has highly stochastic nature. LonWorks-IP based VDN induced transmission delay deteriorates the performance and stability of the real-time distributed control system and can't give an effective preventive and predictive maintenance. In order to guarantee the stability and performance of the system, and give an effective preventive and predictive maintenance, LonWorks-IP based VDN induced time-varying uncertain time delay needs to be predicted and compensated. In this paper new Pill control scheme based on Smith predictor, disturbance observer and band pass filter is proposed and tested through computer simulation about position control of DC servo motor. It is shown that how can the proposed control scheme be designed to minimize the effects of uncertain varying time delay and model uncertainties. The validity of the proposed control scheme is compared and demonstrated with the comparison of internal model controllers(IMC) based on Smith predictor with and without disturbance observer.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.169-174
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• 2000

In this paper, Haar wavelet-based neural network is described for the identification and control of discrete-time nonlinear dynamical systems. Wavelets are suited to depict functions with local nonlinearities and fast variations because of their intrinsic properties of finite support and self-similarity. Due to the orthonormal properties of Haar wavelet functions, wavelet neural networks result in a greatly simplified training problem. This wavelet-based scheme performs adaptively both the identification of nonlinear functions and the control of the overall system, while the multilayer neural network is applied to the control system just after its sufficient learning of the unknown functions. Simulation shows that the wavelet network can be a good alternative to a multilayer neural network with backpropagation.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2521-2525
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• 2005

In this paper, we presented a predictive control technique, which is based on wavelet neural network (WNN), for the control of chaotic systems whose precise mathematical models are not available. The WNN is motivated by both the multilayer feedforward neural network definition and wavelet decomposition. The wavelet theory improves the convergence of neural network. In order to design predictive controller effectively, the WNN is used as the predictor whose parameters are tuned by error between the output of actual plant and the output of WNN. Also the training method for the finding a good WNN model is the Extended Kalman algorithm which updates network parameters to converge to the reference signal during a few iterations. The benefit of EKF training method is that the WNN model can have better accuracy for the unknown plant. Finally, through computer simulations, we confirmed the performance of the proposed control method.

• 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.

• The KIPS Transactions:PartC
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• v.11C no.4
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• pp.429-438
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• 2004

This Paper Presents the control structure for incoming traffic from arbitrary node to Provide admission control in policy-based W network management structure using fuzzy logic control approach. The proposed control structure uses scheme for deciding network resource allocation depending on requirements predefined-policies and network states. The proposed scheme enhances policy adapting methods of existing binary methods, and can use resource of network more effectively to provide adaptive admission control, according to the unpredictable network states for predefined QoS policies. Simulation results show that the proposed controller improves the ratio of packet rejection up to 26%, because it Performs the soft adaption based on the network states instead of accept/reject action in conventional CAC(Connection Admission Controller).

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.6
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• pp.295-301
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• 2013

In this paper, we propose a real-time centralized soft motion control system for high speed and precision robot control. The system engages EtherCAT as high speed industrial motion network to enable force based motion control in real-time and is composed of software-based master controller with PC and slave interface modules. Hard real-time control capacity is essential for high speed and precision robot control. To implement soft based real time control, The soft based master controller is designed using a real time kernel (RTX) and EtherCAT network, and servo processes are located in the master controller for centralized motion control. In the proposed system, slave interface modules just collect and transfer all sensor information of robot to the master controller via the EtherCAT network. It is proven by experimental results that the proposed soft motion control system has real time controllability enough to apply for various robot control systems.