• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 1999년도 추계종합학술대회
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• pp.395-400
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• 1999

DNC(Direct Numerical Control) Network을 위한 프로그램을 효율적으로 하기위해 현재 많은 시스템들이 개발되어 사용되고 있다. 그러나 이 시스템들은 원거리 상의 컴퓨터와 머시닝센터간의 상호 연결이 원만하지 않아 작업에 비효율적인 면이 있고, 또 머시닝 센터에서의 데이터 송 수신에서 일어나는 오류 문제에 대한 시스템으로의 적절한 대처를 할 수가 없다는 문제점이 있다. 그래서, 본 논문에서는 DNC Network을 통해 리C(Numerical Control) 선반 제어기에서 컴퓨터의 데이터를 오류 없이 수신 가능한 데이터 원격 제어 시스템을 새로이 구성하였다. 이 데이터 원격 제어 시스템의 주요 장점으로는 머시닝 센터에서 운영자가 쉽게 컴퓨터에 저장된 NC 데이터 호출과 송출이 자유롭고, 컴퓨터와 공작기계간의 상호 대화가 없이도 NC 기계상에서의 원격 제어(Remote Control)가 가능하다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.1783-1784
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• 2007

A new method to control a remote robot with PDA and wireless network is presented. A robot system and PDA are used to show the effectiveness of the control system in home network environments.

• 한국자동차공학회논문집
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• 제10권4호
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• pp.175-180
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• 2002

In order to satisfy the requirements of time reduction and cost saving for development of electronic control systems(ECU) in automotive industry, the applications of a standardized real-time operating system(RTOS) and a communication protocol to ECUs are increased. In this study, a body control module(BCM) that employs OSEK/VDX(open system and corresponding interfaces for automotive electronics/vehicle distributed executive) OS tour the RTOS and a controller area network(CAN) fur the communication protocol is designed, and the performances of the system are evaluated. The BCM controls doors, mirrors, and windows of the vehicle through the in-vehicle network. To identify all the transmitted and received control messages, a PC connected with the CAN communication protocol behaves as a CAN bus emulator. The control system based upon in-vehicle network improves the system stability and reduces the number of wiring harness. Furthermore it is easy to maintain and simple to add new features because the system is designed based on the standards of RTOS and communication protocol.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.1860-1861
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• 2006

In this paper, we design a robust controller of biped robot system with uncertainties, using recurrent neural network. In our proposed control system, we use the self-recurrent wavelet neural network (SRWNN). The SRWNN makes up for the weak points in wavelet neural network(WNN). While the WNN has fast convergence ability, it dose not have a memory. So the WNN cannot confront unexpected change of the system. However, the SRWNN, having advantage of WNN such as fast convergence, can easily encounter the unexpected change of the system. For stable walking control of biped robot, we use sliding mode control (SMC). Here, uncertainties are predicted by SRWNN. The weights of SRWNN are trained by adaptive laws based on Lyapunov stability theorem. Finally, we carry out computer simulations with a biped robot model to verify the effectiveness of the proposed control system,.

• 대한기계학회논문집A
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• 제28권7호
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• pp.955-960
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• 2004

In this paper, a neural network controller that can be implemented in parallel with a PD controller is suggested for motion control of a hydraulic servo cylinder. By applying a self-excited oscillation method, the system design parameters of open loop transfer function of servo cylinder system are identified. Based on system design parameters, the PD gains are determined for the desired closed loop characteristics. The Neural Network is incorporated with PD control in order to compensate the inherent nonlinearities of hydraulic servo system. As an application example, a motion control using PD-NN has been performed and proved its superior performance by comparing with that of a PD control.

• 제어로봇시스템학회논문지
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• 제14권1호
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• pp.1-6
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• 2008

The ubiquitous sensor network technique is widely applied to variety of information fields such as home automations, logistics, traffic controls, public administrations, health and environment monitoring and etc. It is particularly useful in the areas where energy consumption is minimal and where continuous monitoring of the surrounding environments, which generates streams of data, are required. In this study, we have designed and implemented a living environment automatic control system which collects the streams of temperature, humidity, light and noise data of a simulated house setting in real-time fashion, then controls the home environment based on the collected data according to the users favorites. In order to differentiate the proposed system from the currently existing similar system, we have demonstrated not only the feasibility of collecting data using sensor network in the controlled environment but also the ability to control the various household equipments through wireless communications.

• Journal of Advanced Marine Engineering and Technology
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• 제23권2호
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• pp.168-175
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• 1999

Unlike a general inverted pendulum system which is moved on the cart the proposed inverted pendulum system in this paper has an inverted pendulum which is moved on the two-degree-of-freedom parallelogram link. The dynamic equation of the pendulum system activated by the DD(Direct Drive)motor includes many nonlinear terms and has the high degree of freedoms. The problem is followed hat the exact mathmatical equations can not be analized by a general linear theory However the neural network trained by a simple learning method can control the dynamic system with hard nonlinearities. Learning procedure is the backpropagation algorithm with super-visory signal. The plant inputs obtained by the designed neural network in this paper can stabilize the pendu-lem and get the servo control. Experiment results have proce the effectiveness of the designed neural network controller.

• International Journal of Control, Automation, and Systems
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• 제3권2호
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• pp.202-210
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• 2005

This paper presents a new neural network control for nonlinear bridge systems with earthquake excitation. We design multi-layer neural network controllers with a single hidden layer. The selection of an optimal number of neurons in the hidden layer is an important design step for control performance. To select an optimal number of hidden neurons, we progressively add one hidden neuron and observe the change in a performance measure given by the weighted sum of the system error and the control force. The number of hidden neurons which minimizes the performance measure is selected for implementation. A neural network was trained for mitigating vibrations of bridge systems caused by El Centro earthquake. We applied the proposed control approach to a single-degree-of-freedom (SDOF) and a two-degree-of-freedom (TDOF) bridge system. We assessed the robustness of the control system using randomly generated earthquake excitations which were not used in training the neural network. Our results show that the neural network controller drastically mitigates the effect of the disturbance.

• International Journal of Control, Automation, and Systems
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• 제3권4호
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• pp.612-619
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• 2005

A robust adaptive speed sensorless induction motor direct torque control (DTC) using a neural network (NN) is presented in this paper. The inherent lumped uncertainties of the induction motor DTC system such as parametric uncertainty, external load disturbance and unmodeled dynamics are approximated by the NN. An additional robust control term is introduced to compensate for the reconstruction error. A control law and adaptive laws for the weights in the NN, as well as the bounding constant of the lumped uncertainties are established so that the whole closed-loop system is stable in the sense of Lyapunov. The effect of the speed estimation error is analyzed, and the stability proof of the control system is also proved. Experimental results as well as computer simulations are presented to show the validity and efficiency of the proposed system.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권11호
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• pp.741-746
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• 2004

When investigating a control problem for network based control systems, the main issue is network-induced delay. This delay can degrade the performance of control systems designed without considering the delay and even destabilize the system. In this paper, we consider the stabilization of network based control systems, where there is bounded time-varying delay. This delay is treated like parameter variation of a discrete time system. The state feedback controller design is formulated as linear matrix inequality. Finally, we show that the stability of control systems designed with considering the delay is superior to that is not so.