• 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.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.403-405
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• 2004

This paper describes an implementation of Home network Control Protocol(HnCP) and it's application to an Intelligent lighting system. The HnCP was announced by korea PLC forum in June 2003 to provide a network protocol for PLC based home appliances. The HnCP master and HnCP slaves were implemented using XPLC30 which is an SOC with ARM9 core. The efficacy of the developed HnCP network modules were shown by applying them to a intelligent lighting system composed of dimmable fluorescent lamps. An extended message set was proposed for the intelligent lighting system and we proposed some directions for the future development of HnCP.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1716-1722
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes bacteria foraging algorithm based optimal learning fuzzy-neural network (BA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by bacteria foraging algorithm. The learning algorithm of the BA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, bacteria foraging algorithm is used for tuning of membership functions of the proposed model.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.4
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• pp.15-26
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• 1998

Full car model is needed for investigating as a entire dynamics of vehicle. In this study, 7DOF of full car model's dynamics is selected. This paper proposes the output feedback controller based on optimal control theory. Input data and output data from the optimal controller are used for neural network system identification of the suspension system. To do system identification, neural network which has robustness against nonlinearities and disturbances is adapted. This study uses back-propagation algorithm to train a multil-layer neural network. After obtaining a neural network model of a suspension system, a neuro-controller is designed. Neuro-controller controls suspension system with off-line learning method and multistep ahead prediction model based on the neural network model and a neuro-controller. The optimal controller and the neuro-controller are designed and then, both performances are compared through. For simulation, sinusoidal and rectangular virtual bumps are selected.

• Journal of Ocean Engineering and Technology
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• v.8 no.2
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• pp.131-140
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• 1994

Dynamics of AUV has heavy nonlinearities and many unknown parameters due to its bluff shape and low cruising speed. Intelligent algorithms, therefore, are required to overcome these nonlinearities and unknown system dynamics. Several identification techniques have been suggested for the application of control of underwater vehicles during last decade. This paper applies the neural network to identification and motion control problem of AUVs. Nonlinear dynamic systems of an AUV are identified using feedforward neural network. Simulation results show that the learned neural network can generate the motion of AUV. This paper, also, suggest an adaptive control scheme up-dates the controller weights with reference model and feedforward neural network using error back propagation.

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.3
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• pp.9-18
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• 2008

Mobile devices do not need the fixed network infrastructure in ad-hoc network, these devices communicate each other through the distributed control. Accordingly, mobile devices can discover several services using dynamic searching method and provide safely public ownership of these services. Ad-hoc network needs the distributed control and topology of dynamic network because the limited power for processing and network communication. This paper is devoted to provide the secure protocol that provides efficient services discovery using SDP(Service Discovery Protocol) and considers the security requirements. Proposed protocol provides the distributed control based on PKI without central server, the discovery of trusted service, secure telecommunication, the identification among mobile devices, and service access control by user authority.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.1
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• pp.18-28
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• 1993

Processes in the real-time distributed control systems share a network medium to exchange their data. Performance of feedback control loops in the real-time distributed control systems is subject to the network-induced delays from sensor to controller, and from controller to actuator. The network-induced delays are directly dependent upon the data sampling times of control components which share a network medium. In this study, an algorithm of determining data sampling times is developed using the "window concept". where the sampling datafrom the control components dynamically share a limited number of windows. The scheduling algorithm is validated through the aimulation experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2155-2158
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• 2003

A term of home automation that was in fashion only a few years ago has not been used any more. Nowadays, We have been used a term of home network or digital home than home automation much. While internet infra is diffused at home, data network corp., communication corp., electric appliance corp. and home automation control system corp. which we did not mind each other particularly constructed consortium, and they have designs on home network market. Also, cellular phone's growth tried home networking by using not only wired internet but also broadband wireless communication. Regardless, many solutions are coming out it is few to be applied to real life because the standard is not determined with the protocol each other. Therefore, we developed home network system using USB(Universal Serial Bus) that has the possibility most in home networking standard. The mobile USB home control system is excellent at expansibility and portability. Also we can complete low cost and stable system using an embedded system.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.560-563
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• 2002

This paper proposes a home control system architecture that have network configuration function. The proposed home control system architecture is implemented with partly hardware and software. For implementation of this system architecture, we developed ECONICS which is home automation controller. ECONICS consists of main board and communication modem. This communication modem supply the power line communication. The physical layer and the MAC layer software of EIA 709.1 standard protocol are implemented in communication modem. The upper layer software of EIA 709.1 standard protocol and home configuration software for home network installation, management, diagnostics, control and monitoring are implemented in main board of ECONICS. We verified the commercial feasibility of the proposed system through the home network configuration and operation. As a result, we have concluded that the proposed home control system architecture provides all the key function necessary to easily manage and control home network nodes.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.200-209
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• 1999

This paper presents a precise tracking control scheme for the proposed parallel robot using artificial neural network. This control scheme is composed of three feedback controllers and one feedforward controller. Conventional PD controller and artificial neural network are used as feedback and feedforward controller respectively. A backpropagation learning strategy is applied to the training of artificial neural network, and PD controller outputs are used as target outputs. The PD controllers are designed at the robot dynamics based on inter-relationship between active joints and moving platform. Feedback controllers insure the total stability of system, and feedforward controller generates the control signal for trajectory tracking. The precise tracking performance of proposed control scheme is proved by computer simulation.