• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.409-412
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• 2007

Demand for the technology for access to device control network in industry and for access to building automation system via internet is on the increase. In such technology integration of a device control network with a data network such as internet and organizing wide-ranging DCS(distributed control system) is needed, and it can be realized in the framework of VDN(virtual device network). Specifications for device control network and data network are quite different because of the differences in application. So a router that translates the communication protocol between device control network and data network, and efficiently transmits information to destination is needed for implementation of the VDN(virtual device network). This paper proposes the concept of NCS(networked control system) based on VDN(virtual device network) and suggests the routing algorithm that uses embedded system.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2002.03a
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• pp.219-227
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• 2002

In this paper, we designed neural network predictive PID controller to control sway happened in transfer of trolley for automatic travel control system. We include dynamic character of nonlinear system, and mathematical expression veny simple used neural network. When various establishment location and surrounding disturbance were approved based on mathematical modelling of crane, controller designed to become effective control location error and vibration angle of two control variables that simultaneously can predictive control. Neural network predictive PID controller produced parameter of PID controller using neural network self-tuner. Neural network self-tuner's input used crane's output and neural network predictive output. Neural network self-tuner using error back propagation algorithm. We analyzed control performance comparison through computer simulation when applied disturbance about sway of location and angle in transfer of crane. The results show that the proposed neural network predictive PID controller has better performances than general PID controller, neural network PID controller.

• International Journal of Automotive Technology
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• v.7 no.6
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• pp.687-695
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• 2006

This paper presents a network-based traction control system(TCS), where several electric control units(ECUs) are connected by a controller area network(CAN) communication system. The control system consists of four ECUs: the electric throttle controller, the transmission controller, the engine controller and the traction controller. In order to validate the traction control algorithm of the network-based TCS and evaluate its performance, a Hardware-In-the-Loop Simulation(HILS) environment was developed. Herein we propose a new concept of the HILS environment called the network-based HILS(Net-HILS) for the development and validation of network-based control systems which include smart sensors or actuators. In this study, we report that we have designed a network-based TCS, validated its algorithm and evaluated its performance using Net-HILS.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.309-314
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.364-367
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• 2008

Many different types of network equipments are deployed in a large-scale IP network. In this operating environment, network service providers suffer from difficulty in controlling various equipments simultaneously in case network faults happen in their overall or regional network due to physical link failure or abnormal traffic. This paper presents a policy-based methodology to control many different types of network equipments at the same time in abnormal cases. The key idea is that NMS(Network Management System) keeps vendor-neutral control policies in normal times and that when an abnormal case occurs in network, NMS transforms the selected policy into vendor-specific control commands and enforces them to various equipments simultaneously.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1558-1562
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• 2005

This paper describes the design and implementation of a wireless home network system using Home Network Control Protocol (HNCP) called the wireless HNCP home network system. For wireless interfaces of HNCP, IEEE 802.11b and IEEE 802.15.4 standard protocols are considered. With the implementation of the wireless HNCP home network system, a simple analysis about coexistence between IEEE 802.11b and IEEE 802.15.4 is achieved. Through the implemented wireless HNCP home network system and the analytical results about the coexistence between both two different wireless protocols, the feasibility of the wireless HNCP home network system is shown.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.58-58
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• 2000

As many sensors and actuators are used in various automated systems, the application of network system to real-time distributed control is gaining acceptance in many industries. In order to take advantages of the network technique. however, network implementation should be carefully designed to satisfy real-time constraints and to consider network delays. This paper presents the implementation of feedback control system in Profibus-DP. Profibus-DP is a type of fieldbus protocols that are specifically designed to interconnect simple devices with fast I/O data exchange. As feedback control in profibus-DP is implemented, Network delays is found with influence of system performance. We analyze network delays in Profibus-DP into 3 reasons - dead time in Profibus interface, protocol delay, delay by asynchronization. In order to compensate the network delays, we introduce control algorithms with time delay concept. The results show that network delay can be compensated.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.4
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• pp.400-410
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• 1992

This paper deals with a design of neural network control architectures for robot motion. Three types of control architectures are designed as follows : 1) a neural network control architecture which has the same characteristics as computed torque method 2) a neural network control architecture for compensating the control error on computed torque method with fixed feedback gain 3) neural network adaptive control architecture. Computer simulation of PUMA manipulator with 6 links is conducted for robot motion in order to examine the proposed neural network control architectures.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2292-2294
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• 2002

This paper presents the network of Distributed Control System(DCS) considering specification of nuclear power plant. The network is composed of field network, control network and information network. The protocol of control network is ring type and it is compared to ethernet type. This paper proposes the structure of DCS, the protocol of each network and analyzes the network traffic along data capacity of field network, control network, information network and the network performance. Network II.5 is used as traffic simulation tool.