• 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 IEEK Conference
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• 2003.07c
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• pp.2807-2810
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• 2003

In this paper, we propose a fuzzy control method for improving the control performance by automatically tuning the scaling factor. The proposed method is that automatically tune the input scaling factor and the output scaling factor of fuzzy logic system through neural network. Used neural network is ADALINE (ADAptive Linear NEron) neural network with delayed input. ADALINE neural network has simple construct, superior learning capacity and small computation time. In order to verify the effectiveness of the proposed control method, we performed simulation. The results showed that the proposed control method improves considerably on the environment of the disturbance.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.355-355
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• 2000

The position control accuracy of a robot manipulator is significantly deteriorated when a long arm robot is operated at a high speed. This paper presents a very simple sliding mode control which eliminates multiple mode residual vibration in a 개bot manipulator. The neural network is used to avoid that sliding mode condition is deviated due to the change of system parameter and disturbance. This paper is suggested control system which designed by sliding mode controller using neural network. The effectiveness of proposed scheme is demonstrated through computer simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.9
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• pp.767-777
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• 2012

Topology control increases channel efficiency by controlling transmission power of a node, and as a result, network lifetime and throughput are increased. However, reducing transmission range causes a network connectivity problem, especially in mobile networks. When a network loses connectivity, the network topology should be re-configured. However, topology re-configuration consumes lots of energy because every node need to collect neighbor information. As a result, network lifetime may decrease, even though topology control is being used to prolong the network lifetime. Therefore, network connectivity time needs to be increased to expend network lifetime in mobile networks. In this paper, we propose an Adaptive-Redundant Transmission Range (A-RTR) algorithm to address this need. A-RTR uses a redundant transmission range considering a node status and flexibly changes a node's transmission range after a topology control is performed.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1319-1327
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• 2015

As wind power farm (WPF) installations continue to grow, monitoring and controlling large-scale WPFs presents new challenges. In this paper, a hierarchical network architecture is proposed in order to provide remote monitoring and control of large-scale WPFs. The network architecture consists of three levels, including the WPF comprised of wind turbines and meteorological towers, local control center (LCC) responsible for remote monitoring and control of wind turbines, and a central control center (CCC) that offers data collection and aggregation of many WPFs. Different scenarios are considered in order to evaluate the performance of the WPF communications network with its hierarchical architecture. The communications network within the WPF is regarded as the local area network (LAN) while the communication among the LCCs and the CCC happens through a wide area network (WAN). We develop a communications network model based on an OPNET modeler, and the network performance is evaluated with respect to the link bandwidth and the end-to-end delay measured for various applications. As a result, this work contributes to the design of communications networks for large-scale WPFs.

• Journal of Power Electronics
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• v.12 no.3
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• pp.468-476
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• 2012

The interior permanent magnet synchronous motor (IPMSM) has been widely used in electric vehicle applications due to its excellent power to weigh ratio. This paper proposes the maximum torque control of an IPMSM drive using an adaptive learning (AL) fuzzy neural network (FNN) and an artificial neural network (ANN). This control method is applicable over the entire speed range while taking into consideration the limits of the inverter's rated current and voltage. This maximum torque control is an executed control through an optimal d-axis current that is calculated according to the operating conditions. This paper proposes a novel technique for the high performance speed control of an IPMSM using AL-FNN and ANN. The AL-FNN is a control algorithm that is a combination of adaptive control and a FNN. This control algorithm has a powerful numerical processing capability and a high adaptability. In addition, this paper proposes the speed control of an IPMSM using an AL-FNN, the estimation of speed using an ANN and a maximum torque control using the optimal d-axis current according to the operating conditions. The proposed control algorithm is applied to an IPMSM drive system. This paper demonstrates the validity of the proposed algorithms through result analysis based on experiments under various operating conditions.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.444-452
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• 2008

We propose an intelligent predictive control approach for a nonlinear networked control system (NCS) with time-varying delay and random observation. The control is given by the sum of a nominal control and a corrective control. The nominal control is determined analytically using a linearized system model with fixed time delay. The corrective control is generated online by a neural network optimizer. A Markov chain (MC) dynamic Bayesian network (DBN) predicts the dynamics of the stochastic system online to allow predictive control design. We apply our proposed method to a satellite attitude control system and evaluate its control performance through computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.121.6-121
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• 2002

Web-based virtual machine/manufacturing system (VMS) utilizes Virtual Device Network (VDN.) VDN inevitably involves the implementation of Distributed Monitoring and Control Networks (DMCN). In general, one needs to integrate device (control) network and IP network to realize DMCN over IP network or internet, which can be viewed as a VDN. In this study, LonWorks networking technology is used for device network and the transmission characteristics of LonWorks/IP-based VDN is investigated. A method to minimize the transmission delay in the LonWorks/IP networks is also suggested.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1868-1870
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• 2006

Neural network is used in many fields of control systems, but input-output patterns of a control system are not easy to be obtained and by using as single feedback neural network controller. And also it is difficult to get a satisfied performance when the changes of rapid load and disturbance are applied. To resolve those problems, this paper proposes a new algorithm which is the neural network controller. The new algorithm uses the neural network instead of activation function to control object at the output node. Therefore, control object is composed of neural network controller unifying activation function, and it supplies the error back propagation path to calculate the error at the output node. As a result, the input-output pattern problem of the controller which is resigned by the simple structure of neural network is solved, and real-time learning can be possible in general back propagation algorithm. Application of the new algorithm of neural network controller gives excellent performance for initial and tracking response and it shows the robust performance for rapid load change and disturbance. The proposed control algorithm is implemented on a high speed DSP, TMS320C32, for the speed of 3-phase induction motor. Enhanced performance is shown in the test of the speed control.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.11
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• pp.127-139
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• 1996

In this paper we propose a new controller design method using hybrid fuzzy-neural netowrk and neural network identification in order ot control systems which are more and more getting nonlinearity. Proposed method performs, for a nonlinear plant with unknown functions, hybird identification using a fuzzy-neural network and a neural network, and then a stable nonlinear controller is designed with those identified informations. To identify a nonlinear function, which is directly related to input signals, we can use a neural network which is satisfied with the proposed stable condition. To identify a nonlinear function, which is not directly related to input signals, we can use a fuzzy-neural network which has excellent identification characteristics. In order to verify excellent control performances of the proposed method, we compare the porposed control method with a conventional neural network control method through simulations and experiments with one link manipulator.