• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.791-793
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• 1999

In this paper, neural network-supervisory control method is proposed to minimize the effect of system uncertainty by load change and disturbance in the PID control system. In the proposed method, PID controller performs main control action by performing control within constraint error. And neural network-supervisory controller performs control action when error reaches the boundary of constraint error. Combining neural network-supervisory controller to guarantee the stability into PID control system, the resulting PID control system is expected to show better performance in the system with load change and disturbance. Simulation applying PID controller and neural network-supervisory controller showed excellence of proposed method.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1996년도 추계학술대회 논문
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• pp.133-137
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• 1996

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have bevome increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking arre indispensable capabilities for their versatile application. the need to meet demanding control requirement in increasingly complex dynamical control systems under sygnificant uncertainties leads toward design of implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme the ntworks intrduced are neural nets with dynamic neurouns whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure fast in computation and suitable for implementation of real-time control, Performance of the neural controller is illustrated by simulation and experimental results for a SCAEA robot.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.100-103
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• 1999

In this paper, a method for the efficient implementation of the PICNET network configurator for a distributed control system(DCS) is proposed. The network configurator is composed of the time parameter estimator and the period scheduler, the file generator. The main role of network configurator estimates time parameter, the pre-run time scheduling of the user input and make the period transmission table for operating the PICNET based distributed control system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2341-2344
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• 2001

In recent years the neural network known as a sort of the intelligent control strategy is used as a powerful tool for designing control system since it has learning ability. But it is difficult for neural network controllers to guarantee the stability of control systems. In this paper we try connecting a radial basis function network to an adaptive control strategy. Radial basis function networks are simpler and easier to handle than multilayer perceptrons. We use the radial basis function network to generate control input signals that are similar to the control inputs of adaptive control using linear reparameterization of the robot manipulator. We adopt the saturation function as an auxiliary controller. This paper also proves mathematically the stability of the control system under the existence of disturbances and modeling errors.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권9호
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• pp.638-643
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• 2004

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 paper 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. Also this paper implements the security of the home network system based on the UPnP (Universal Plug and Play), adding VPN (Virtual Private Network) router that uses the IPsec to the home network system which is consisted of the ARM9 and the Embedded Linux.

• International Journal of Control, Automation, and Systems
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• 제6권1호
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• pp.24-34
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• 2008

A novel neural network model, termed the standard neural network model (SNNM), similar to the nominal model in linear robust control theory, is suggested to facilitate the synthesis of controllers for delayed (or non-delayed) nonlinear systems composed of neural networks. The model is composed of a linear dynamic system and a bounded static delayed (or non-delayed) nonlinear operator. Based on the global asymptotic stability analysis of SNNMs, Static state-feedback controller and dynamic output feedback controller are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based nonlinear systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Two application examples are given where the SNNMs are employed to synthesize the feedback stabilizing controllers for an SISO nonlinear system modeled by the neural network, and for a chaotic neural network, respectively. Through these examples, it is demonstrated that the SNNM not only makes controller synthesis of neural-network-based systems much easier, but also provides a new approach to the synthesis of the controllers for the other type of nonlinear systems.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제50권1호
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• pp.37-44
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• 2001

This paper describes the position control method in yard crane drive system using Lonworks network, which is a leading industrial control network. The network is composed of host computer and three motor drive systems for both gantry and trolley position controls of both gantry and trolley are controlled with the simulator of yard crane, the size of which is about 1/10 with the real yard crane.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 I
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• pp.69-72
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• 2003

This paper presents a traffic control agent that can perform the dynamic resource allocation by controlling traffic flows on a DiffServ network. In addition, this paper presents a router that can support DiffServ on Linux to support selective QoS in IP network environment. To implement a method for selective traffic transmission based on priority on a DiffServ router, this paper changes the queuing discipline in Linux, and presents the traffic control agent so that it can efficiently control routers, efficiently allocates network resources according to service requests, and relocate resources in response to state changes of the network.

• 한국통신학회논문지
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• 제33권11B호
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• pp.961-967
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• 2008

Congestion control is one of major mechanisms to avoid dropped packets. Many researchers use optimization theories to find an efficient way to reduce congestion in networks, but they do not consider robustness that may lead to unstable network utilities. This paper proposes a new methodology in order to solve a congestion control problem for wired networks by using a robust design principle. In our particular numerical example, the proposed method provides robust solutions that guarantee high and stable network utilities.

• 한국산업융합학회 논문집
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• 제18권4호
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• pp.259-266
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• 2015

This study presents new scheme for various walking pattern of biped robot under the limitted enviroments. We show that the neural network is significantly more attractive intelligent controller design than previous traditional forms of control systems. A multilayer backpropagation neural network identification is simulated to obtain a learning control solution of biped robot. Once the neural network has learned, the other neural network control is designed for various trajectory tracking control with same learning-base. The main advantage of our scheme is that we do not require any knowledge about the system dynamic and nonlinear characteristic, and can therefore treat the robot as a black box. It is also shown that the neural network is a powerful control theory for various trajectory tracking control of biped robot with same learning-vase. That is, we do net change the control parameter for various trajectory tracking control. Simulation and experimental result show that the neural network is practically feasible and realizable for iterative learning control of biped robot.